SPM

1041

Health, Wellbeing and

the Changing Structure

of Communities

Coordinating Lead Authors: Guéladio Cissé (Mauritania/Switzerland/France), Robert McLeman

(Canada)

Lead Authors: Helen Adams (United Kingdom), Paulina Aldunce (Chile), Kathryn Bowen (Australia),

Diarmid Campbell-Lendrum (United Kingdom), Susan Clayton (USA), Kristie L. Ebi (USA), Jeremy

Hess (USA), Cunrui Huang (China), Qiyong Liu (China), Glenn McGregor (United Kingdom/

New Zealand), Jan Semenza (Sweden), Maria Cristina Tirado (USA/Spain)

Contributing Authors:

Ibidun Adelekan (Nigeria), Ayansina Ayanlade (Nigeria), Nicola Banwell

(Australia), Ritwika Basu (India/United Kingdom), Lea Berrang-Ford (United Kingdom/Canada),

Rachel Bezner Kerr (Canada/USA), Robbert Biesbroek (Netherlands), Halvard Buhaug (Norway),

Katrin Burkart (USA), Mercedes Bustamante (Brazil), Luisa Cabeza (Spain), Martina Angela

Caretta (Sweden), Edwin Castellanos (Guatemala), So-Min Cheong (Republic of Korea),

Winston Chow (Singapore), Mark John Costello (New Zealand/Norway/Ireland), Marlies Craig

(South Africa), Felix Creutzig (Germany), Ashlee Cunsolo (Canada), Michael Davies (United

Kingdom), David Dodman (United Kingdom), Susan Elliott (Canada), Siri Eriksen (Norway),

Maria Figueroa (Denmark/Venezuela), François Gemenne (Belgium), Elisabeth Gilmore (USA/

Canada), Bruce Glavovic (South Africa/New Zealand), Sherilee Harper (Canada), Nathalie Hilmi

(Monaco), John Ji (China), Rhys Grifﬁth Jones (New Zealand), Felix Kanungwe Kalaba (Zambia),

Saori Kitabatake (Japan), Krishna Krishnamurthy (Mexico), Ronald Law (Philippines), Stefanie

Langsdorf (Germany), Walter Leal (Germany), Adrian Leip (Italy), Elena Lopez-Gunn (Spain/

United Kingdom), Wei Ma (China), Angelo Maggiore (Italy), Amina Maharjan (Nepal), Júlia

Alves Menezes (Brazil), Sebastian Mirasgedis (Greece), Naho Mirumachi (Japan), Ruth Morgan

(Australia), Rupa Mukerji (Switzerland/India), Aditi Mukherji (India), Virginia Murray (United

Kingdom), Jacques Andre Ndione (Senegal), Hannah Tait Neufeld (Canada), Peter Newman

(Australia), Lena Maria Nilsson (Sweden), Nick Obradovich (Germany), Ben Orlove (USA), Jennifer

J Otten (USA), Camille Parmesan (France/United Kingdom/USA), Karishma Patel (USA), Mark

Pelling (United Kingdom), Revati Phalkey (India), Elvira Poloczanska (United Kingdom), Marie-

Fanny Racault (United Kingdom/France), Diana Reckien (Germany/Netherlands), Joacim Rocklöv

(Sweden), Sharma Rohit (India), Andrea Rother (South Africa), Yamina Saheb (France/Algeria),

Sonia Salas (Chile), Gerardo Sanchez Martinez (Spain), Amiera Sawas (United Kingdom), Daniel

Schensul (USA), Corinne Schuster-Wallace (Canada), Sam Sellers (USA), Chandni Singh (India),

1042

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Pramod Kumar Singh (India), Yona Sipos (USA/Canada), Peter Smith (United Kingdom), Marco

Springmann (Germany), Jeff Stanaway (USA), Stavana E. Strutz (USA), Dhar Subash (Denmark/

India), Janet Swim (USA), Philip Thornton (United Kingdom), Christopher Trisos (South Africa),

Diana Urge-Vorsatz (Hungary), Maarten van Aalst (Netherlands), Jose Luis Vivero Pol (Italy), Olivia

Warrick (New Zealand), Nick Watts (Australia), Alistair Woodward (New Zealand), David Wrathall

(USA), Zinta Zommers (Latvia)

Review Editors: Bettina Menne (Italy/Germany), Sergey Semenov (Russian Federation), Jean-

François Toussaint (France)

Chapter Scientists: Christopher Boyer (USA), Nikhil Ranadive (USA)

This chapter should be cited as:

Cissé, G., R. McLeman, H. Adams, P. Aldunce, K. Bowen, D. Campbell-Lendrum, S. Clayton, K.L. Ebi, J. Hess, C. Huang,

Q. Liu, G. McGregor, J. Semenza, and M.C. Tirado, 2022: Health, Wellbeing, and the Changing Structure of Communities.

In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment

Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska,

K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University

Press, Cambridge, UK and New York, NY, USA, pp. 1041–1170, doi:10.1017/9781009325844.009.

1043

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Table of Contents

Executive Summary �� 1044

7.1 Introduction

�� 1048

7.1.1 Major Health-Related Statements in AR5

�� 1048

7.1.2 Major Statements About Migration and Conﬂict

in AR5

�� 1048

7.1.3 Important Developments Since AR5

�� 1048

7.1.4 Interpretation of ‘Health and Well-Being’ Used in

This Chapter

�� 1049

7.1.5 Towards Socioecological Perspectives on Health,

Well-Being, and Loss and Damage

�� 1049

7.1.6 Developments Relevant to Tracking and Assessing

Climate Change Impacts on Health

�� 1049

7.1.7 Hazards, Exposure and Vulnerability in the Context

of Human Health, Well-Being and the Changing

Structure of Communities

�� 1050

Box7.1 | Indigenous Peoples’ Health and Well-Being

in a Changing Climate

�� 1054

7.1.8 Visual Guide to this Chapter

�� 1059

7.2. Observed Impacts of Climate Change on Health,

Well-Being, Migration and Conﬂict

�� 1059

7.2.1 Observed Impacts on Health and Well-Being

�� 1059

Box7.2 | The Global Burden of Climate-Sensitive

Health Outcomes Assessed in this Chapter

�� 1060

7.2.2 Observed Impacts on Communicable Diseases

1062

Box7.3 | Cascading Risk Pathways Linking Waterborne

Disease to Climate Hazards

�� 1065

Cross-Chapter BoxCOVID | COVID-19

�� 1067

7.2.3 Observed Impacts on Non-communicable

Diseases

�� 1071

7.2.4 Observed Impacts on Other Climate-Sensitive

Health Outcomes

�� 1072

7.2.5 Observed Impacts on Mental Health and

Well-Being

�� 1076

7.2.6 Observed Impacts on Migration

�� 1079

Cross-Chapter BoxMIGRATE | Climate-Related

Migration

�� 1080

Box7.4 | Gender Dimensions of Climate-Related

Migration

�� 1085

7.2.7 Observed Impacts of Climate on Conﬂict

�� 1086

7.3 Projected Future Risks under Climate Change

� 1089

7.3.1 Projected Future Risks for Health and

Well-Being

�� 1089

7.3.2 Migration and Displacement in a Changing

Climate

�� 1099

Box7.5 | Uncertainties in projections of future

demographic patterns at global, regional and national

scales

�� 1101

7.3.3 Climate Change and Future Risks of Conﬂict

�� 1102

7.4 Adaptation to Key Risks and Climate Resilient

Development Pathways

�� 1102

7.4.1 Adaptation Solution Space for Health and

Well-Being

�� 1103

7.4.2 Adaptation Strategies, Policies and

Interventions

�� 1105

7.4.3 Enabling Conditions and Constraints for Health

Adaptation

�� 1115

7.4.4 Migration and Adaptation in the Context of

Climate Change

�� 1116

7.4.5 Adaptation Solutions for Reducing Conﬂict

Risks

�� 1118

7.4.6 Climate Resilient Development Pathways

�� 1119

Cross-Chapter BoxHEALTH | Co-beneﬁts of Climate

Actions for Human Health, Well-Being and Equity

�� 1124

Frequently Asked Questions

FAQ 7.1 | How will climate change affect physical

and mental health and well-being?

�� 1126

FAQ 7.2 | Will climate change lead to wide-scale

forced migration and involuntary displacement?

�� 1128

FAQ 7.3 | Will climate change increase the potential

for violent conﬂict?

�� 1128

FAQ 7.4 | What solutions can effectively reduce climate

change risks to health, well-being, forced migration

and conﬂict?

�� 1128

FAQ 7.5 | What are some speciﬁc examples of actions

taken in other sectors that reduce climate change

risks in the health sector?

�� 1129

Acknowledgements

�� 1130

References

�� 1130

1044

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Executive Summary

Climate-related illnesses, premature deaths, malnutrition in all its

forms, and threats to mental health and well-being are increasing

(very high conﬁdence

). Climate hazards are a growing driver of

involuntary migration and displacement (high conﬁdence) and are

a contributing factor to violent conﬂict (high conﬁdence). These

impacts are often inter-connected, are unevenly distributed across and

within societies, and will continue to be experienced inequitably due

to differences in exposure and vulnerability (very high conﬁdence).

Cascading and compounding risks affecting health due to extreme

weather events have been observed in all inhabited regions, and risks

are expected to increase with further warming (very high conﬁdence)

{7.1.3, 7.1.4; Cross-Chapter BoxCOVID in Chapter 7; 7.2.1, 7.2.2, 7.2.3,

7.2.4, 7.3.1, 7.3.2, 7.3.3, 7.4.1, 7.4.4; Cross-Chapter Box HEALTH in

Chapter 7; Cross-Chapter BoxILLNESS in Chapter 2}.

Since AR5, new evidence and awareness of current impacts

and projected risks of climate change on health, well-being,

migration and conﬂict have emerged, including greater

evidence of the detrimental impacts of climate change on

mental health (very high conﬁdence). New international agreements

were reached on climate change (Paris Agreement), disaster risk

reduction (DRR) (Sendai Agreement), sustainable development (the

Sustainable Development Goals (SDGs)), urbanisation (The New

Urban Agenda), migration (Global Compact for Safe, Orderly and

Regular Migration) and refugees (Global Compact on Refugees) that,

if achieved, would reduce the impacts of climate change on health,

well-being, migration and conﬂict (very high conﬁdence). However, the

challenges with implementing these agreements are highlighted by

the coronavirus disease 2019 (COVID-19) pandemic, which exposed

systemic weaknesses at community, national and international levels

in the ability of societies to anticipate and respond to global risks

(high conﬁdence). Incremental changes in policies and strategies

have proven insufﬁcient to reduce climate-related risks to health,

well-being, migration and conﬂict, highlighting the value of more

integrated approaches and frameworks for solutions across systems

and sectors that are embodied in these new international agreements

(high conﬁdence) {7.1.3, 7.2.1, 7.4.1, 7.4.2, 7.4.3, 7.4.6; Cross-Chapter

BoxCOVID in Chapter 7}.

With proactive, timely and effective adaptation, many risks

for human health and well-being could be reduced and some

potentially avoided (very high conﬁdence). A signiﬁcant adaptation

gap exists for human health and well-being and for responses to disaster

risks (very high conﬁdence). Nationally Determined Contributions

(NDCs) to the Paris Agreement from low- and middle-income countries

identify health as a priority concern. National planning on health and

climate change is advancing, but the comprehensiveness of strategies

and plans need to be strengthened, and implementing action on

key health and climate change priorities remains challenging (high

conﬁdence). Multi-sectoral collaboration on health and climate change

policy is evident, with uneven progress, and ﬁnancial support for health

1 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of conﬁdence is

expressed using ﬁve qualiﬁers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium conﬁdence. For a given evidence and agreement statement, different conﬁdence levels

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing conﬁdence.

adaptation is only 0.5% of dispersed multi-lateral climate ﬁnance

projects (high conﬁdence). This level of investment is insufﬁcient to

protect population health and health systems from most climate-

sensitive health risks (very high conﬁdence) {7.4.1, 7.4.2, 7.4.3}.

Climate resilient development has a strong potential to

generate substantial co-beneﬁts for health and well-being and

to reduce risks of involuntary displacement and conﬂict (very

high conﬁdence). Sustainable and climate resilient development

that decreases exposure, vulnerability and societal inequity and

that increases timely and effective adaptation and mitigation more

broadly, has the potential to reduce but not necessarily eliminate

climate change impacts on health, well-being, involuntary migration

and conﬂict (high conﬁdence). This development includes greenhouse

gas (GHG) emission reductions through clean energy and transport;

climate-resilient urban planning; sustainable food systems that lead

to healthier diets; universal access to healthcare and social protection

systems; wide-scale, proactive adaptive capacity building for climate

change; and achievement of the SDGs (very high conﬁdence). Meeting

the objectives of the Global Compact for Safe, Orderly, and Regular

Migration and building inclusive and integrative approaches to

climate-resilient peace would help prevent health risks related to

migration and conﬂict (high agreement, medium evidence). The net

global ﬁnancial gains from these co-beneﬁts to health and well-

being, including avoided hospitalisations, morbidity and premature

deaths, exceed the ﬁnancial costs of mitigation (high conﬁdence).

As an example of co-beneﬁts, the ﬁnancial value of health beneﬁts

from improved air quality alone is projected to be greater than the

costs of meeting the goals of the Paris Agreement (high conﬁdence).

All pathways to climate resilient development, including those for the

health and healthcare systems, involve balancing complex synergies

and trade-offs between development pathways and the options that

underpin climate mitigation and adaptation pathways (very high

conﬁdence) {7.4.6; Cross-Chapter Box HEALTH in Chapter 7; Cross-

Chapter BoxMIGRATE in Chapter 7}.

Key transformations are needed to facilitate climate

resilient development pathways (CRDPs) for health, well-

being, migration and conﬂict avoidance (high conﬁdence). The

transformational changes will be more effective if they are

responsive to regional, local and Indigenous knowledge and

consider the many dimensions of vulnerability, including those

that are gender- and age-speciﬁc (high conﬁdence). A key pathway

towards climate resilience in the health sector is universal access to

primary healthcare, including mental healthcare (high conﬁdence).

Investments in other sectors and systems that improve upon the social

determinants of health have the potential to reduce vulnerability to

climate-related health risks (high conﬁdence). Links between climate

risks, adaptation, migration and labour markets highlight the value

of providing better mobility options as part of transformative change

(medium conﬁdence). Strong governance and gender-sensitive

approaches to natural resource management can reduce the risk of

inter-group conﬂict in climate-disrupted areas (medium conﬁdence)

1045

Health, Wellbeing and the Changing Structure of Communities Chapter 7

{7.4.6; Cross-Chapter Box COVID in Chapter 7; Cross-Chapter

BoxHEALTH in Chapter 7; Cross-Chapter BoxGENDER in Chapter 18;

Cross-Chapter BoxINDIG in Chapter 18; Cross-Chapter BoxMIGRATE

in Chapter 7}.

Observed Impacts

Climate hazards are increasingly contributing to a growing

number of adverse health outcomes (including communicable

and non-communicable diseases (NCDs)) in multiple geographical

areas (very high conﬁdence). The net impacts are largely negative at

all scales (very high conﬁdence), and there are very few examples

of beneﬁcial outcomes from climate change at any scale (high

conﬁdence). While malaria incidence has declined globally due to non-

climatic socioeconomic factors and health system responses, a shift to

higher altitudes has been observed as the climate warms (very high

conﬁdence). Climate variability and change (including temperature,

relative humidity and rainfall) and population mobility are signiﬁcantly

and positively associated with observed increases in dengue globally;

chikungunya virus in Asia, Latin America, North America and Europe (high

conﬁdence); Lyme disease vector Ixodes scapularis in North America

(high conﬁdence); and Lyme disease and tick-borne encephalitis vector

Ixodes ricinus in Europe (medium conﬁdence). Higher temperatures (very

high conﬁdence), heavy rainfall events (high conﬁdence) and ﬂooding

(medium conﬁdence) are associated with an increase of diarrhoeal

diseases in affected regions, including cholera (very high conﬁdence),

other gastrointestinal infections (high conﬁdence) and food-borne

diseases due to Salmonella and Campylobacter (medium conﬁdence).

Floods have led to increases in vector- and waterborne diseases and

to disturbances of public health services (high conﬁdence). Climate

extremes increase the risks of several types of respiratory tract infections

(high conﬁdence). Climate-related extreme events such as wildﬁres,

storms and ﬂoods are followed by increased rates of mental illness in

exposed populations (very high conﬁdence) {7.2.1, 7.2.2, 7.2.3, 7.2.4,

7.2.5, 7.2.6}.

Several chronic, non-communicable respiratory diseases are

climate-sensitive based on their exposure pathways (e.g.,

heat, cold, dust, small particulates, ozone, ﬁre smoke and

allergens) (high conﬁdence), although climate change is not the

dominant driver in all cases. Worldwide, rates of adverse health

impacts associated with small particulate matter (PM) exposure have

decreased steadily due to decreasing primary emissions (very high

conﬁdence), while rates of adverse health impacts from ozone air

pollution exposure have increased (very high conﬁdence). Exposure to

wildland ﬁres and associated smoke has increased in several regions

(very high conﬁdence). Spring pollen season start dates in northern

mid-latitudes are occurring earlier due to climate change, increasing

the risks of allergic respiratory diseases (high conﬁdence) {7.2.3.2}.

Heat is a growing health risk due to burgeoning urbanisation,

an increase in high temperature extremes and demographic

changes in countries with aging populations (very high conﬁdence).

Potential hours of work lost due to heat has increased signiﬁcantly

over the past two decades (high conﬁdence). Some regions are already

experiencing heat stress conditions at or approaching the upper limits

of labour productivity (high conﬁdence). A signiﬁcant proportion of

warm-season heat-related mortality in temperate regions is linked

to observed anthropogenic climate change (medium conﬁdence) but

greater evidence is required for tropical regions. For some heatwave

events over the last two decades, associated health impacts can be at

least partially attributed to observed climate change (high conﬁdence).

Extreme heat has negative impacts on mental health, well-being,

life satisfaction, happiness, cognitive performance and aggression

(medium conﬁdence) {7.2.4.1, 7.2.4.5}.

Climate variability and change contribute to food insecurity,

which can lead to malnutrition, including undernutrition,

overweight and obesity, and to disease susceptibility in low- and

middle-income countries (high conﬁdence). Populations exposed

to extreme weather and climate events may consume inadequate or

insufﬁcient food, leading to malnutrition and increasing the risk of

disease (high conﬁdence). Children and pregnant women experience

disproportionately greater adverse nutrition and health impacts (high

conﬁdence). Climatic inﬂuences on nutrition are strongly mediated by

socioeconomic factors (very high conﬁdence) {7.2.4.4, 7.3.1}.

Extreme climate events act as both direct drivers (e.g.,

destruction of homes by tropical cyclones) and as indirect

drivers (e.g., rural income losses during prolonged droughts) of

involuntary migration and displacement (very high conﬁdence).

Most documented examples of climate-related displacement occur

within national boundaries, with international movements occurring

primarily within regions, particularly between countries with

contiguous borders (high conﬁdence). Global statistics collected since

2008 by the Internal Displacement Monitoring Centre (IDMC) show

an annual average of over 20million people internally displaced by

weather-related extreme events, with storms and ﬂoods the most

common drivers (high conﬁdence). The largest absolute number of

people displaced by extreme weather each year occurs in Asia (South,

Southeast and East), followed by sub-Saharan Africa, but small island

states in the Caribbean and South Paciﬁc are disproportionately affected

relative to their small population size (high conﬁdence). Immobility in

the context of climate risks can reﬂect vulnerability and lack of agency

but can also be a deliberate choice of people to maintain livelihoods,

economic considerations and social and cultural attachments to place

(high conﬁdence) {7.2.6; Cross-Chapter BoxMIGRATE in Chapter 7}.

Climate hazards have affected armed conﬂict within countries

(medium conﬁdence), but the inﬂuence of climate is small

compared to socioeconomic, political and cultural factors (high

conﬁdence). Climate increases conﬂict risk by undermining food

and water security, income and livelihoods in situations where there

are large populations, weather-sensitive economic activities, weak

institutions and high levels of poverty and inequality (high conﬁdence).

In urban areas, food and water insecurity and inequitable access to

services has been associated with civil unrest where there are weak

institutions (medium conﬁdence). Climate hazards are associated

with increased violence against women, girls and vulnerable

groups, and the experience of armed conﬂict is gendered (medium

conﬁdence). Adaptation and mitigation projects implemented without

consideration of local social dynamics have exacerbated non-violent

conﬂict (medium conﬁdence) {7.2.7}.

1046

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Projected Risks and Vulnerabilities

A signiﬁcant increase in ill health and premature deaths from

climate-sensitive diseases and conditions is projected due to

climate change (high conﬁdence). An excess of 250,000 deaths

–1

by 2050 attributable to climate change is projected due to heat,

undernutrition, malaria and diarrhoeal disease, with more than half of

this excess mortality projected for Africa (compared to a 1961–1991

baseline period for a mid-range emissions scenario) (high conﬁdence).

Risks for heat-related morbidity and mortality, ozone-related mortality,

malaria, diseases carried by Aedes sp. mosquitoes, Lyme disease and

West Nile fever, as well as the temperatures at which risk transitions

occur (i.e. from moderate to high to very high), are contingent on

future development pathways (high conﬁdence) {7.3.1}.

Climate change is projected to signiﬁcantly increase population

exposure to heatwaves (very high conﬁdence) and heat-related

morbidity and mortality (high conﬁdence). Models suggest exposure

increases 16 times under Representative Concentration Pathway

(RCP)4.5 and 36 times under RCP8.5, with the impact of warming

ampliﬁed under development pathways that do not foster sustainable

development. Globally, the impact of projected climate change on

temperature-related mortality is expected to be a net increase under

RCP4.5 to RCP8.5, even with adaptation (high conﬁdence). Heat

related cardiovascular disease mortality is projected to increase by the

end of this century (high conﬁdence). Strong geographical differences

in heat-related mortality are projected to emerge later this century,

mainly driven by population growth and aging in regions with tropical

and subtropical climates (very high conﬁdence) {7.3.1}.

The burdens of several climate-sensitive food-borne, waterborne,

and vector-borne diseases (VBDs) are projected to increase under

climate change, assuming no additional adaptation (very high

conﬁdence). The distribution and intensity of transmission of malaria is

expected to decrease in some areas and increase in others, with increases

projected mainly along the current edges of its geographic distribution

in endemic areas of sub-Saharan Africa, Asia and South America (high

conﬁdence). Dengue risk will increase, with a larger spatio-temporal

distribution in Asia, Europe and sub-Saharan Africa under RCP6.0 and

RCP8.5, potentially putting another 2.25 billion people at risk (high

conﬁdence). Higher incidence rates are projected for Lyme disease in

the Northern Hemisphere (high conﬁdence) and for transmission of

Schistosoma mansoni in eastern Africa (high conﬁdence) {7.3.1; Cross-

Chapter BoxILLNESS in Chapter 2}.

Increasing atmospheric concentrations of carbon dioxide and

climate change are projected to increase diet-related risk

factors and related non-communicable diseasess globally

and increase undernutrition, stunting and related childhood

mortality particularly in Africa and Asia, with outcomes

depending on the extent of mitigation and adaptation (high

conﬁdence). These projected changes are expected to slow progress

towards eradication of child undernutrition and malnutrition (high

conﬁdence). Higher atmospheric concentrations of carbon dioxide

reduce the nutritional quality of wheat, rice and other major crops,

potentially affecting millions of people at a doubling of carbon dioxide

(very high conﬁdence) {7.3.1}.

Climate change is expected to have adverse impacts on

well-being and to further threaten mental health (very high

conﬁdence). Children and adolescents, particularly girls, elderly people,

and people with existing mental, physical and medical challenges are

particularly at risk. Mental health impacts are expected to arise from

exposure to high temperatures, extreme weather events, displacement,

malnutrition, conﬂict, climate-related economic and social losses, and

anxiety and distress associated with worry about climate change (very

high conﬁdence) {7.3.1.11}.

Future climate-related migration is expected to vary by region

and over time, according to future climatic drivers, patterns of

population growth, adaptive capacity of exposed populations

and international development and migration policies (high

conﬁdence). The wide range of potential outcomes is reﬂected in model

projections of population displacements by 2050 in Latin America, sub-

Saharan Africa and south Asia due to climate change, which vary from

31 million to 143 million people, depending on assumptions about

future GHG emissions and socioeconomic development trajectories

(high conﬁdence). With every additional one degree Celsius of

warming, the global risks of involuntary displacement due to ﬂood

events are projected to rise by approximately 50% (high conﬁdence).

High emissions/low development scenarios raise the potential

for higher levels of migration and involuntary displacement (high

conﬁdence) and increase the need for planned relocations and support

for people exposed to climate extremes but lacking the means to move

(high conﬁdence) {7.3.2; Cross-Chapter BoxMIGRATE in Chapter 7}.

Climate change may increase susceptibility to violent conﬂict,

primarily intra-state conﬂicts, by strengthening climate-sensitive

drivers of conﬂict (medium conﬁdence). Future violent conﬂict risk

is highly mediated by socioeconomic development trajectories (high

conﬁdence) and so trajectories that prioritise economic growth,

political rights and sustainability are associated with lower conﬂict risk

(medium conﬁdence). Future climate change may exceed adaptation

limits and generate new causal pathways not observed under current

climate variability (medium conﬁdence). Economic shocks are not

included in many models of conﬂict risks currently used, and some

projections do not incorporate known socioeconomic predictors of

conﬂict (medium conﬁdence). As such, future increases in conﬂict-

related deaths with climate change have been estimated, but results

are inconclusive (medium conﬁdence).

Solutions

Since AR5, the value of cross-sectoral collaboration to advance

sustainable development has been more widely recognised,

but despite acknowledgement of the importance of health

adaptation as a key component, action has been slow (high

conﬁdence). Building climate-resilient health systems will require

multi-sectoral, multi-system and collaborative efforts at all governance

scales (very high conﬁdence) (Sections7.4.1, 7.4.2). Globally, health

systems are poorly resourced in general, and their capacity to

respond to climate change is weak, with mental health support being

particularly inadequate (very high conﬁdence). The health sectors of

some countries have focused on implementing incremental changes to

policies and measures to ﬁll the adaptation gap (very high conﬁdence).

1047

Health, Wellbeing and the Changing Structure of Communities Chapter 7

As the likelihood of dangerous risks to human health continue to

increase, there is greater need for transformational changes to health

and other systems (very high conﬁdence). This highlights an urgent

and immediate need to address the wider interactions between

environmental change, socioeconomic development and human health

and well-being (high conﬁdence) {7.4.1, 7.4.2, 7.4.3}.

Targeted investments in health and other systems, including

multi-sectoral, integrated approaches to protect against key

health risks can effectively increase resilience (high conﬁdence).

Increased investment in strengthening general health systems, along

with targeted investments to enhance protection against speciﬁc

climate-sensitive exposures (e.g., hazard early warning and response

systems, and integrated vector control programmes for VBDs) will

increase resilience if implemented to at least keep pace with climate

change (high conﬁdence).

• The future effects of climate change on VBDs can be signiﬁcantly

offset through enhanced commitment to and implementation

of integrated vector control management approaches, disease

surveillance, early warning systems and vaccine development (very

high conﬁdence) (Sections7.4.1, 7.4.2).

• Adaptation options for future climate risks associated with

waterborne and food-borne diseasess include improving access

to potable water, reducing exposure of water and sanitation

systems to ﬂooding and extreme weather events, and improved

(including expanded) early warning systems (very high conﬁdence)

(Sections7.4.1, 7.4.2).

• Adaptation options for future extreme heat risks include heat

action plans (HAPs) that incorporate early warning and response

systems for urban and non-urban settings; tried, tested and

iteratively updated response strategies targeting both the general

population and vulnerable groups such as older adults or outside

workers; and effective stakeholder communication plans (high

conﬁdence). These short-term responses can be complemented by

longer-term urban planning and design, including nature-based

solutions (NbS) that mitigate urban heat island (UHI) effects (high

conﬁdence) (Sections7.4.1, 7.4.2, 7.4.3).

• Adaptation options to reduce the future risks of malnutrition

include access to healthy, affordable, diverse diets from sustainable

food systems (high conﬁdence); health services including maternal,

child and reproductive health (high conﬁdence); nutrition services,

nutrition and shock sensitive social protection (high conﬁdence);

water, sanitation and early warning systems (high conﬁdence); and

risk reduction schemes such as insurance (medium conﬁdence)

(Section7.4.2.1.3).

The COVID-19 pandemic has demonstrated the value of

coordinated and multi-sectoral planning, social protection

systems, safety nets and other capacities in societies to cope

with a range of shocks and stresses (high conﬁdence). The

pandemic has posed a severe shock to many socioeconomic systems,

resulting in substantial changes in vulnerability and exposure of

people to climate risks (high conﬁdence). The pandemic emphasises

the inter-connected and compound nature of risks, vulnerabilities, and

responses to emergencies that are simultaneously local and global

(high conﬁdence). Pathways to climate resilient development can be

pursued simultaneously with recovering from the COVID-19 pandemic

(high conﬁdence). The COVID-19 pandemic has aggravated climate

risks, demonstrated the global and local vulnerability to cascading

shocks and illustrated the importance of integrated solutions that

tackle ecosystem degradation and structural vulnerabilities in human

societies (high conﬁdence) {Cross-Chapter BoxCOVID in Chapter 7}.

Transitioning towards equitable, low-carbon societies has

multiple beneﬁts for health and well-being (very high conﬁdence).

Beneﬁts for health and well-being can be gained from wide-spread,

equitable access to affordable renewable energy (high conﬁdence);

active transport (e.g., walking and cycling) (high conﬁdence); green

buildings and nature-based solutions, such as green and blue urban

infrastructure (high conﬁdence); and by transitioning to a low-carbon,

well-being-oriented and equity-oriented economy consistent with the

aims of the SDGs (high conﬁdence). Plant-rich diets consistent with

international recommendations for healthy diets could contribute to

lower GHG emissions while also generating health co-beneﬁts, such

as reducing ill health related to over-consumption of animal-based

products (high conﬁdence) {7.4.2; Cross-Chapter Box HEALTH in

Chapter 7; 7.4.4}.

Reducing future risks of involuntary migration and displacement

due to climate change is possible through cooperative

international efforts to enhance institutional adaptive capacity

and sustainable development (high conﬁdence). Institutional and

cross-sectoral efforts to build adaptive capacity, coupled with policies

aimed at ensuring safe and orderly movements of people within and

between states, can form part of the CRDPs that reduce future risks

of climate-related involuntary migration, displacement and immobility

(medium conﬁdence). In locations where permanent, government-

assisted relocation becomes unavoidable, active involvement of local

populations in planning and decision-making increases the likelihood

of successful outcomes (medium conﬁdence). People who live on small

island states do not view relocation as an appropriate or desirable

means of adapting to the impacts of climate change (high conﬁdence)

{7.4.3; Cross-Chapter BoxMIGRATE in Chapter 7}.

Adaptation and sustainable development build peace in conﬂict-

prone regions by addressing the drivers of grievances that lead

to conﬂict and vulnerability to climate change (high conﬁdence).

Environmental peacebuilding (EP) through natural resource sharing,

conﬂict-sensitive adaptation and climate-resilient peacebuilding offer

promising avenues for addressing conﬂict risk, but their efﬁcacy is still

to be demonstrated through effective monitoring and evaluation (high

conﬁdence). Formal institutional arrangements for natural resource

management contribute to wider cooperation and peacebuilding

(high conﬁdence) and gender-based approaches provide under-utilised

pathways to achieving sustainable peace (medium conﬁdence).

Inclusion, cross-issue and cross-sectoral integration in policy and

programming, and approaches that incorporate different geographical

scales and work across national boundaries can support climate-

resilient peace (high conﬁdence) {7.4.5, 7.4.6}.

1048

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

7.1 Introduction

This chapter assesses peer-reviewed and selected grey literature

published since the IPCC’s Fifth Assessment Report (AR5) on the

impacts and projected future risks of climate change for health, well-

being, migration and conﬂict, taking into consideration determinants

of vulnerability and the dynamic structure of human populations and

communities. Particular attention is given to potential adaptation

challenges and actions as well as potential co-beneﬁts for health

associated with mitigation actions. AR5 presented strong evidence-

based statements regarding the likely

impacts of climate change on

health, migration and conﬂict in two separate chapters on Human Health

(Chapter 11) and Human Security (Chapter 12). The present chapter

covers all topics found in AR5 Chapter 11 and Sections12.4 (Migration

and Mobility Dimensions of Human Security), 12.5 (Climate Change and

Armed Conﬂict) and 12.6 (State Integrity and Geopolitical Rivalry) and

provides an additional, expanded assessment of mental health impacts,

gender dimensions of climate risks and solution pathways.

7.1.1 Major Health-Related Statements in AR5

AR5 stated with very high conﬁdence that the health of human

populations is sensitive to climate change (Smith et al., 2014).

Speciﬁc observations of current impacts included the expansion of the

geographical ranges of some diseases into previously unaffected areas

and changes in the distributions of some food-, water- and vector-

borne diseases (high conﬁdence). Increasing future health risks were

projected from injury, disease and death due to more intense heatwaves

and ﬁres (very high conﬁdence), undernutrition in poor regions (high

conﬁdence), food- and waterborne diseases (very high conﬁdence)

and VBDs (medium conﬁdence). AR5 found that climate change is a

multiplier of existing health vulnerabilities, including food insecurity

and limited access to safe water, improved sanitation, healthcare and

education, and that the most effective measures to reduce vulnerability

in the near term are programmes that implement and improve basic

public health (very high conﬁdence). Opportunities for co-beneﬁts from

mitigation actions were identiﬁed through such actions as reducing

local emissions of short-lived climate pollutants from energy systems

(very high conﬁdence) and expanding transport systems that promote

active travel (high conﬁdence). The signiﬁcant growth in peer-reviewed

publications on links between climate change and human health and

well-being since AR5 allowed for a more detailed and wider reaching

assessment in the present chapter and stronger conﬁdence statements

for many climate-sensitive health outcomes.

7.1.2 Major Statements About Migration and Conﬂict in

AR5

Key statements made in AR5 Chapter 12 (Human Security) about the

impacts of climate change on migration were that climate change

will have signiﬁcant impacts on forms of migration that compromise

2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as

likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not >50–100%, and Extremely unlikely

0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome

lies within the 17–83% probability range.

human security and that mobility is a widely used strategy to maintain

livelihoods in response to social and environmental changes (high

agreement, medium evidence). Research on the inﬂuence of climate

change and climate extremes on multiple forms of migration (including

voluntary migration, involuntary displacement and immobility) has

expanded signiﬁcantly since AR5, which has allowed for a more robust

assessment in this chapter, with migration also featuring in most other

sectoral and regional chapters of this report. With respect to violent

conﬂict, AR5 Chapter 12 found that people living in places affected by

violent conﬂict are particularly vulnerable to climate change (medium

evidence, high agreement), that some of the factors increasing the risk

of violent conﬂict within states are sensitive to climate change (medium

evidence, medium agreement) and that climate change will lead to

new challenges to states and will increasingly shape both conditions

of security and national security policies (medium evidence, medium

agreement). As with other subjects assessed in this chapter, there

has been signiﬁcant growth in the number of assessable studies, but

there remain shortcomings with respect to the availability of evidence

regarding the speciﬁc nature of causal linkages and the attributability

of particular outcomes to climate events or conditions.

7.1.3 Important Developments Since AR5

7.1.3.1 International Agreements

Since AR5, several new international agreements have come into

effect that have implications for international responses to the

climate risks assessed in this chapter. The 2015 Paris Agreement,

which explicitly mentions health in three separate sections, set new

goals for adaptation and established a working group to study the

effects of climate change on population displacement. The 17 United

Nations (UN) SDGs for 2030, adopted in 2015, are all important for

building adaptive capacity in general, with goals 13 (‘Take urgent

action to combat climate change and its impacts’) and 3 (‘Ensure

healthy lives and promote well-being for all at all ages’) being directly

relevant to this chapter. Other SDGs contain speciﬁc targets that are

also relevant for this chapter, including Target 10.7 (‘Well-managed

migration policies’), Target 8.3 (‘Decent work for all’) and Target 5.4

(‘Promotion of peaceful and inclusive societies’) (Piper, 2017). The

2015 Sendai Framework for Disaster Risk Reduction puts an emphasis

on health and well-being (Aitsi-Selmi and Murray, 2016) In 2018, UN

member states negotiated Global Compacts for Safe, Orderly and

Regular Migration and on Refugees that, taken together with the Paris

Agreement, provide pathways for coordinated international responses

to climate-related migration and displacement (Warner, 2018).

7.1.3.2 IPCC Special Reports

All three post-AR5 IPCC Special Reports considered some of the

research that is assessed here in greater detail. The 2018 report on

1.5°C (SR1.5) included a review of climate change and health literature

published since AR5 and called for further efforts for protecting health

1049

Health, Wellbeing and the Changing Structure of Communities Chapter 7

and well-being of vulnerable people and regions (Ebi etal., 2018b)

and highlighted links between climate change hazards, poverty, food

security, migration and conﬂict. The 2019 Special Report on Climate

Change and Land (SRCCL) (IPCC, 2019b) emphasised the impacts of

climate change on food security; highlighted links between reduced

resilience of dryland populations, land degradation, migration and

conﬂict; and raised concerns about the impacts of climate extremes.

The 2019 Special Report on the Ocean and Cryosphere in a Changing

Climate (IPCC, 2019a) detailed how changes in the cryosphere

and ocean systems have impacted people and ecosystem services,

particularly food security, water resources, water quality, livelihoods,

health and well-being, infrastructure, transportation, tourism and

recreation as well as the culture of human societies, particularly for

Indigenous Peoples. It also noted the risks of future displacements due

to rising sea levels and associated coastal hazards.

7.1.4 Interpretation of ‘Health and Well-Being’ Used in

This Chapter

Assessing the links between human health, well-being and climate

change is a new task for AR6, reﬂecting a broad perspective on health

that increasingly acknowledges the importance of well-being and its

interactions with individual and population health. The World Health

Organization (WHO) deﬁnes health as ‘a state of complete physical,

mental and social well-being and not merely the absence of disease or

inﬁrmity’ (WHO, 1946). Although this chapter assesses physical health,

mental health and general well-being separately, they are inter-

connected; any type of health problem can reduce overall well-being

and vice versa. For example, a child receiving inadequate nutrition may

not be sick but is experiencing a clear threat to well-being that has

implications for future physical and mental health.

There is no consensus deﬁnition of well-being, but it is generally agreed

that it includes a predominance of positive emotions and moods (e.g.,

happiness) compared with extreme negative emotions (e.g., anxiety),

satisfaction with life, a sense of meaning and positive functioning,

including the capacity for unimpaired cognitive functioning and

economic productivity (Diener and Tay, 2015; Piekałkiewicz, 2017).

A capabilities approach (Sen, 2001) focuses on the opportunity for

people to achieve their goals in life (Vik and Carlquist, 2018) or the

ability to take part in society in a meaningful way, and is reﬂected

in personal freedoms, human agency, self-efﬁcacy, ability to self-

actualise, dignity and relatedness to others (Markussen etal., 2018).

An indigenous perspective on well-being is broad and typically

incorporates a healthy relationship with the natural world (Sangha

etal., 2018); emotional and mental health have also been linked to

a strong cultural identity (Butler etal., 2019; Dockery, 2020). ‘Health’

itself is sometimes described as including relationships between

humans and nature as well as links to community and culture

(Donatuto etal., 2020; Dudgeon etal., 2017).

Subjective well-being is consistently associated with personal indicators

such as higher income, greater economic productivity, better physical

health (Diener and Tay, 2015; Delhey and Dragolov, 2016; De Neve

etal., 2013) and environmental health, and it is reﬂected in societal

indicators such as social cohesion and equality (Delhey and Dragolov,

2016). In a global survey of over 1 million people taken between

2004 and 2008 via the Gallup World Poll, annual income and access

to food were strong predictors of subjective well-being and a healthy

environment. In particular, access to clean water was important even

when household income was controlled (Diener and Tay, 2015). Access

to green spaces was also closely associated with well-being (high

conﬁdence) (Lovell etal., 2018; Yuan etal., 2018).

7.1.5 Towards Socioecological Perspectives on Health,

Well-Being, and Loss and Damage

Since AR5, more comprehensive frameworks for framing and studying

global health issues, including planetary health, ‘One Health’ and eco-

health, have gained traction. These frameworks share an ecological

perspective, emphasise the role of complex systems and highlight the

need for inter-disciplinary approaches related to human health research

and practice (Lerner and Berg, 2015; Zinsstag etal., 2018; Whitmee

et al., 2015; Steffen et al., 2015). These frameworks increasingly

shape the evidence related to climate change health impacts and

response options, highlight the dynamics of complex systems in risk

management and direct risk management efforts in new directions.

Building on these frameworks and perspectives, there is increasing

overlap in the literature on global health, climate change impacts and

estimates of loss and damage. The Global Burden of Disease study

for 2019 for the ﬁrst time included non-optimal temperature as a

risk factor (Murray etal., 2020). Work by social scientists continues

to explore how climate change indirectly affects resource availability,

productivity, migration and conﬂict (Burke etal., 2015a; Carleton and

Hsiang, 2016; Hsiang etal., 2017), bringing multiple lines of inquiry

together to study the associations between global environmental

changes, socioeconomic dynamics and impacts on health and well-

being. Morbidity associated with migration and displacement,

especially in the context of small island states, has been identiﬁed as

a non-material form of loss and damage (Thomas and Benjamin, 2020;

McNamara etal., 2021). Social costs of carbon estimates have been

updated to include excess mortality associated with climate change,

increasing estimates substantially (Dressler, 2021).

7.1.6 Developments Relevant to Tracking and Assessing

Climate Change Impacts on Health

Since AR5, there has been a steady increase in standardised, globally

scoped, data-driven health impact assessments, such as the ongoing

Global Burden of Disease study (James etal., 2018) that now includes

scenario-based projections (Foreman etal., 2018), that make linkages

with other global priorities, including the SDGs (Fullman etal., 2017).

Attention has turned from prioritising speciﬁc diseases like HIV/

AIDS, malaria and tuberculosis, to strengthening health systems

and providing universal health coverage (Chang et al., 2019), with

an ongoing emphasis on the social determinants of health. Several

climate-sensitive health outcomes are now tracked in the annual

Lancet Countdown reports (Watts etal., 2015; Watts etal., 2017; Watts

etal., 2018b; Watts etal., 2019; Watts etal., 2021). The Global Burden

of Disease study is beginning to examine climate-sensitive disease

1050

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

burdens, incorporate temperature as a risk factor (Murray etal., 2020)

and project future cause-speciﬁc disease burdens in a warming world

(Burkart et al., 2021). Although not assessed in this chapter, there

are numerous ongoing assessments of climate change impacts on

health and well-being being undertaken by national and local health

authorities that continue to generate insights into climate-related

health impacts and suggest response options relevant for decision

makers.

While the knowledge base regarding global health has increased, a

comprehensive framework is not in place that fully integrates health,

well-being and environmental impacts from climate change allowing

for the cumulative assessment of their impact. Moreover, signiﬁcant

cracks in the foundation of global health governance that affect

preparedness and adaptive capacity for climate change, among other

threats, have been identiﬁed (Phelan etal., 2020; Defor and Oheneba-

Dornyo, 2020; Ostergard etal., 2020; Shafﬁe, 2021). While attention

to climate change and health has increased and there is evidence of

increasing adaptation activity in the health sector (Watts etal., 2019),

there is also continued evidence of substantial adaptation gaps (UNEP,

2018; UNEP, 2021) including gaps in humanitarian response capacity

for climate-related disasters (Watts et al., 2021) that appear to be

widening as adverse climate change impacts on health and well-being

accrue.

7.1.7 Hazards, Exposure and Vulnerability in the Context

of Human Health, Well-Being and the Changing

Structure of Communities

7.1.7.1 Possible Climate Futures and Hazards

This chapter uses the conceptual framing described in Chapter 1, in

which risks emerging from climate change are described in terms

of hazard, exposure and vulnerability, with adaptation and climate

resilient development being responses that have the potential to

reduce or modify risk. The observed and projected future risks to

health well-being, involuntary population displacements and conﬂict

identiﬁed in this chapter are associated with a range of hazards that

are manifested at a variety of geographical and temporal scales. These

include observed and projected changes in climate normals; changes

in the frequency, duration, and/or severity of extreme events; and

hazards such as rising sea levels and extreme temperatures where the

impacts have only begun to be widely experienced. The 2021 report

of IPCC WGI (IPCC, 2021) provides an assessment of observed and

projected changes in these hazards and is the backdrop against which

assessments of future risks and adaptation options identiﬁed in the

present chapter should be considered.

The exposure to hazards of populations, infrastructure, ecosystem

capital, socioeconomic systems and cultural assets critical to health

and well-being varies considerably across and within regions (high

conﬁdence). Exposure is also projected to vary across and within

regions over time, depending on future GHG emissions pathways,

development trajectories and differential vulnerability, particularly for

exposure to extreme events and conditions, such as ﬂoods and droughts

(Figures7.1a, 7.1b) (Winsemius etal. 2018). For this reason, region-

speciﬁc assessments of climate-related risks for health, displacement

and conﬂict are found in each of the regional chapters of this report in

addition to the general assessment that appears in this chapter.

7.1.7.2 Differential Vulnerability and Cascading Effects

Vulnerability to climate change varies across time and location,

across communities, and among individuals within communities; it

reﬂects variations and changes in macro-scale non-climatic factors

(such as changes in population, economic development, education,

infrastructure, behaviour, technology and ecosystems) and individual-

or household-speciﬁc characteristics, such as age, socioeconomic

status, access to livelihood assets, pre-existing health conditions and

ability (US Global Change Research Program, 2016; Chapter 1).

Many direct and indirect effects of climate change pose multiple

threats to human health and well-being and can occur simultaneously,

resulting in compounding or cascading impacts for vulnerable

populations. For example, many of the long-term impacts of climate

change on NCDs and injury described in Sections 7.2 and 7.3 are

associated with future increases in air temperature and levels of air

pollution; in many regions, and especially in large urban centres in Asia

and Africa, these particular hazards are already causing substantial

increases in morbidity and mortality due to respiratory illnesses (Tong

etal., 2016). Climate change can therefore be expected to magnify

such health risks over the long term.

At the same time, urban populations will also be experiencing indirect

risks through climate change impacts on food and potable water

systems, variations in the distribution and seasonality of infectious

diseases and growing demand for shelter due to increased in-

migration. The accumulation of these risks over time can be expected

to generate accelerating declines in community resilience and health,

with future vulnerability potentially expanding in a nonlinear fashion

(Dilling etal., 2017; Liang and Gong, 2017; El-Zein and Tonmoy, 2017;

see also Chapter 6). Further, although each individual risk in isolation

may be transitory or temporary for the individuals or groups exposed,

taken cumulatively, the impacts could create conditions of chronic

lack of well-being, and early life experiences with speciﬁc illnesses

and conditions could have lifelong consequences (Watts etal., 2015;

Otto etal., 2017; WHO, 2018a). In this context, there is a distinct need

for greater longitudinal research on vulnerability to multiple climatic

and non-climatic health and well-being hazards over time (Fawcett

etal., 2017). There is also need for more research to identify critical

thresholds in social vulnerability to climate change (Otto etal., 2017);

these include rapid, stepwise changes in vulnerability that emerge

from changes in exposure (for example, air temperatures above which

mortality rates or impacts on pre-natal health accelerate (Arroyo

etal., 2016; Ngo and Horton, 2016; Abiona, 2017; Auger etal., 2017;

Molina and Saldarriaga, 2017; Zhang etal., 2017b)) and thresholds in

adaptation processes (such as when rural out-migration rates grow

due to climate-related crop failures (McLeman, 2017)).

In virtually all of the research identifying particular climate-related

risks to health, well-being, migration and conﬂict, speciﬁc types of

individuals are identiﬁed as having higher levels of vulnerability

and exposure to climate-related health hazards: people who are

1051

Health, Wellbeing and the Changing Structure of Communities Chapter 7

impoverished, undernourished, struggle with chronic or repeated

illnesses, live in insecure housing in polluted or heavily degraded

environments, work in unsafe conditions, are disabled, have limited

education and/or have poor access to health and social infrastructure

(WHO, 2018a). Their disproportionate exposure to ongoing climate

hazards and their inability to recover from extreme events increase

not only their own vulnerability but also that of the wider communities

in which they live (US Global Change Research Program, 2016).

Highly vulnerable populations are not evenly distributed across

regions (Figure 7.2) nor within countries. Yet, even those fortunate

enough to live in better neighbourhoods with greater ﬁnancial means,

higher-paying jobs and good access to resources and services, may

experience adverse climate-related outcomes through community-level

interactions and linkages (Haines and Ebi, 2019). Increased inequity

itself threatens well-being and an effective response to climate change

should not only avoid increased inequity but identify ways in which to

reduce existing inequity.

7.1.7.3 Heightened Vulnerability to Climate-Related Impacts on

Health and Well-Being Experienced by Speciﬁc Groups

and Through Speciﬁc Pathways

7.1.7.3.1 Women and Girls

Climate change poses distinct risks to women’s health. Vulnerability

to climate-related impacts on health and well-being shows notable

differentiations according to gender, beyond implications for pregnant

women. In many societies, differential exposure to such risks relate

to gendered livelihood practices and mobility options. Pregnancy and

maternal status heighten vulnerability to heat, infectious diseases,

food-borne infections and air pollution (Arroyo et al., 2016; Ngo

and Horton, 2016; Zhang etal., 2017b). Extreme heat events, high

ambient temperatures, high concentrations of airborne particulates,

water-related illnesses and natural hazards are associated with higher

rates of adverse pregnancy outcomes such as spontaneous abortion,

stillbirth, low birth weight and pre-term birth (Arroyo et al., 2016;

Ngo and Horton, 2016; Abiona, 2017; Auger et al., 2017; Molina

and Saldarriaga, 2017; Zhang etal., 2017b). Women and girls are at

greater risk of food insecurity (FAO, 2018; Alston and Akhter, 2016),

which is particularly problematic in combination with the nutritional

<-50%

>50%

15–25%

-5– 5%

-25– -15%

25–50%

5–15%

-15– -5%

-50– -25%

(b) Projected exposure of poor people to droughts.

Exposure

increase

RCP8.5

2050

Projected exposure of poor people to floods and droughts in selected regions

by 2050 under a high emissions scenario (RCP8.5)

(a) Projected exposure of poor people to floods.

Central and South AmericaAsiaAfrica

Figure7.1 | Projected exposure of poor people to a) ﬂoods respectively b) droughts in selected regions by 2050 under a high emissions scenario (RCP8.5)

(adapted from Winsemius et al., 2018).

1052

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

(a)

Vulnerability at the national level varies. Vulnerability also greatly differs within countries.

Countries with moderate or low average vulnerability have sub-populations with high vulnerability and vice versa.

Relative vulnerability

Medium

Very high

Low

Very low

Population density

Low

High

Observed human vulnerability to climate change is a key risk factor and differs globally

High

Children in rural low-income communities | food insecurity, sensitivity to undernutrition and

disease | 5.12.3

People uprooted by conﬂict in the Near East and Sahel | prolonged temporary status, limited

mobility | Box 8.1, Box 8.4

Women & non-binary | limited access to & control over resources, e.g. water, land, credit |

Box 9.1, CCB-GENDER, 4.8.3, 5.4.2, 10.3.3

Migrants | informal status, limited access to health services & shelter, exclusion from

decision-making processes | 6.3.6, Box 10.2

Aboriginal and Torres Strait Islander Peoples | poverty, food & housing insecurity,

dislocation from community | 11.4.1

People living in informal settlements | poverty, limited basic services & often located in areas

with high exposure to climate hazards | 6.2.3, Box 9.1, 9.9, 10.4.6, 12.3.2, 12.3.5, 15.3.4

Examples of

Indigenous Peoples with

high vulnerability to

climate change and

climate change responses

(4.3.8, 5.10.2, 5.13.5,

Box7.1, 8.2.1, 15.6.4) and

the importance of

Indigenous Knowledge

(Box9.2.1, 11.4, 14.4,

Cross-Chapter Box INDIG)

Indigenous Peoples of the Arctic | health inequality, limited access to subsistence resources and

culture | CCP 6.2.3, CCP 6.3.1

Urban ethnic minorities | structural inequality, marginalisation, exclusion from planning processes

| 14.5.9, 14.5.5, 6.3.6

Smallholder coffee producers | limited market access & stability, single crop dependency, limited

institutional support | 5.4.2

Indigenous Peoples in the Amazon | land degradation, deforestation, poverty, lack of support |

8.2.1, Box 8.6

Older people, especially those poor & socially isolated | health issues, disability, limited access

to support | 8.2.1, 13.7.1, 6.2.3, 7.1.7

Island communities | limited land, population growth and coastal ecosystem degradation | 15.3.2

Examples of vulnerable local groups across different contexts include the following:

Africa

North America

Central &

South America

Australasia

Small islands

Europe

Small islands

Asia

Small islands

10 12

6 12

Figure7.2 | Global distribution of vulnerable people from two indices, with examples (see also Technical Summary, this report).

1053

Health, Wellbeing and the Changing Structure of Communities Chapter 7

needs associated with pregnancy or breastfeeding. Women and girls

are more likely to die in extreme weather events (Garcia and Sheehan,

2016; Yang etal., 2019). Women are also expected to face a greater

mental health burden in a changing climate (Manning and Clayton,

2018). Further, climatic extremes and water scarcity are associated

with increases in violence against girls and women (Anwar etal., 2019;

Opondo etal., 2016; Le Masson etal., 2016; Udas etal., 2019).

7.1.7.3.2 Children

Children are particularly vulnerable to climate change impacts.

Children often have unique pathways of exposure and sensitivity to

climate hazards, given their immature physiology and metabolism

and high intake of air, food and water relative to their body weight

as compared with adults (US Global Change Research Group, 2016).

Climate change is expected to increase childhood risks of malnutrition

and infectious disease for children in low-income countries through its

impacts on household food access, dietary diversity, nutrient quality,

water and changes in maternal and childcare access and breastfeeding

(Tirado, 2017; FAO et al., 2018; Perera, 2017). Children living in

locations with poor sanitation are especially vulnerable to GI illnesses,

with future rates of diarrhoeal diseases among children expected to

rise under many climate change scenarios (Cissé etal., 2018; WHO,

2014). Outdoor recreational opportunities for children may be reduced

by extreme weather events, heat and poor air quality (Evans, 2019).

Children and adolescents are particularly vulnerable to post-traumatic

stress after extreme weather events; the effects may even be long-

lasting, with impacts on their adult functioning (Brown etal., 2017;

UNICEF, 2021; Thiery etal., 2021)

7.1.7.3.3 Elderly

Population age structures and changes over time have a signiﬁcant

inﬂuence on vulnerability to the impacts of weather and climate.

Older adults (generally deﬁned as persons aged 65 and older) are

disproportionately vulnerable to the health impacts associated with

climate change and weather extremes, including a greater risk of

succumbing to waterborne pathogens due to poorer functioning

thermoregulatory mechanisms, greater sensitivity to dehydration,

changes in their immune systems and greater likelihood of having pre-

existing chronic illnesses such as diabetes or respiratory, cardiovascular

and pulmonary illnesses (Benmarhnia etal., 2016; Diaz etal., 2015;

Mayrhuber etal., 2018; Paavola, 2017). Older adults may be less prompt

in seeking medical attention when suffering from GI illnesses, which

can lead to dehydration (Haq and Gutman, 2014). Åström etal. (2017)

anticipate heat-related mortality among the elderly in Europe to rise

in the 2050s under RCP4.5 and RCP8.5 in the absence of signiﬁcant

preventative measures. In a study of the combined effects of warming

temperatures and an aging population in Korea, Lee and Kim (2016)

projected a four- to six-fold increase in heat-related mortality by the

2090s when accounting for temperature and age structure.

7.1.7.3.4 Socioeconomically Marginalised Populations and People

with Disabilities

People living in poverty are more likely to be exposed to extreme

heat and air pollution and have poorer access to clean water and

sanitation, accentuating their exposure to climate change-associated

health risks (UNEP, 2021; FAO etal., 2018). Poverty inﬂuences how

people perceive the risks to which they are exposed, how they

respond to evacuation orders and other emergency warnings and

their ability to evacuate or relocate to a less risk-prone location (US

Global Change Research Program, 2016). Poorer households, who

often live in highly exposed locations, are more likely to be forced

into low-agency migration as a means of adapting to climate risks

and at the same time are the most likely to be immobile or trapped

in deteriorating circumstances where migration would be a preferred

response (Leichenko and Silva, 2014; Fazey etal., 2016; Sheller, 2018).

Climate emergencies disproportionally affect people with disabilities

because of their inherent vulnerabilities, which may impair their

ability to take protective action; they are also frequently excluded

from adaptation planning (Gaskin etal., 2017).

7.1.7.3.5 Urban Compared with Rural Populations

Rural and urban populations are often exposed to different types of

climate-related health risks. For example, because of the UHI effect

and high concentrations of air pollution from motor vehicles and

industrial activity, people who live in urban areas may have higher

rates of extreme heat stress and respiratory illnesses than their rural

counterparts (Hondula etal., 2014; Heaviside etal., 2016; Macintyre

et al., 2018; Schinasi et al., 2018). Conversely, rural populations,

especially those dependent on resource-based livelihoods, may have

a greater exposure to climate impacts on food production or natural

hazard events, which have subsequent effects on household nutrition

and food security (Springmann etal., 2016a; see also Chapters 5 and

6 of this report).

7.1.7.3.6 Indigenous Peoples

Indigenous Peoples, especially those who live in geographically

isolated, resource-dependent and/or impoverished communities, are

often at greater risk of health impacts of climate change (Ford etal.,

2020) (US Global Change Research Program, 2016). The close inter-

connection of land-based livelihoods and cultural identity of many

indigenous groups exposes them to multiple health- and nutrition-

related hazards (Durkalec et al., 2015; Sioui, 2019) with potential

implications for community social relations and individual mental

health (Cunsolo Willox et al., 2013; Cunsolo Willox et al., 2015).

Climate change risk exposures may be complicated by changes in

lifestyle, diet and morbidity driven by socioeconomic processes, further

increasing health risks for Indigenous Peoples (Jaakkola etal., 2018).

Environmental consequences of climate change can also affect social

ties and spiritual well-being, in part because land is often an integral

part of their culture and spiritual identity.

1054

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Box7.1 | Indigenous Peoples’ Health and Well-Being in a Changing Climate

Contributing authors: Hannah Tait Neufeld (Canada), Lena Maria Nilsson (Sweden), Rhys Grifﬁth Jones (New Zealand)

The indigenous population worldwide is estimated at 476million people spread across all geographic regions of the world (FAO etal.,

2021). Indigenous Peoples globally represent a large heterogeneity of people in terms of living conditions and social determinants of

health. There is no simple deﬁnition of who is indigenous. In this text, we refer to Indigenous Peoples as people self-identiﬁed and

organised as indigenous, according to the principles of the International Work Group for Indigenous Affairs (IWGIA), an international non-

governmental organisation (NGO) with observer status at the United Nations (UN). Indigenous Peoples are described as ‘distinct social

and cultural groups that share collective ancestral ties to the lands and natural resources where they live, occupy or from which they have

been displaced’ (World Bank, 2021). A common experience among Indigenous Peoples are historical traumas related to overseas and/or

settler/industrial colonisation.

Studies on climate change as it affects the health of Indigenous Peoples generally focus on non-displaced indigenous groups; that

is, Indigenous Peoples maintaining culturally important elements of a land-based traditional lifestyle. Here we use an eco-medicine

perspective in which the impacts of climate change on health are divided into primary, secondary and tertiary effects, as discussed below

(Butler and Harley, 2010). Many analyses of indigenous health in relation to climate change use the One Health concept (Mackenzie and

Jeggo, 2019; see Section7.1.5).

Current Impacts of Climate Change on Health and Well-Being of Indigenous Peoples

Primary health effects of climate change include the immediate physical effects on human health, such as health hazards due to high

temperatures, extreme weather events or accidents from exposure to climate-related hazards. For example, in arid and semiarid areas,

an increased frequency of severe droughts is associated with immediate health problems related to overheating and lack of water for

drinking, sanitation and livestock (Hall and Crosby, 2020; Mamo, 2020; Rankoana, 2021). In many cases, the possibilities for Indigenous

Peoples to apply traditional strategies to mitigate droughts by migration are limited by competing land use, environmental protection

and national borders, with many examples across Africa (Mamo, 2020). In the Jordan River Valley, the second most water stressed area

in the world, water resources are not equally distributed to Indigenous Bedouin people, amplifying their immediate health threat during

predictable as well as unpredictable droughts (Mamo, 2020).

In Arctic and sub-Arctic areas, higher temperatures with increased numbers of freeze–thaw cycles during the winter means increased

occurrences of transport-related accidents in indigenous communities due to weaker ice on travel routes that cross lakes, rivers and the

sea, along with changes in the snow cover and increased risk of avalanches (Durkalec etal., 2015; Jaakkola etal., 2018). Impeded access

to healthcare during extreme weather conditions is a primary health risk for Indigenous Peoples living in remote areas (Amstislavski etal.,

2013; Hall and Crosby, 2020; Mamo, 2020).

Pastoralists in many regions may experience changes in livestock behaviour due to climate change, leading to increased mobility-related

health hazards (Jaakkola etal., 2018; Mamo, 2020). Indigenous Peoples living in low-lying coastal areas and small island states face long-

term risk of ﬂooding and the stresses of resettlement (Maldonado etal., 2021; McMichael and Powell, 2021).

Extreme rainfall, ﬂooding, storms, heatwaves and wildﬁres lead to individual health hazards that may include injuries and thermal and

respiratory traumas (Mamo, 2020) There are many examples when emergency responses to extreme events have ignored the needs

of displaced Indigenous Peoples (Mendez etal., 2020; Maldonado etal., 2021). Population-based quantitative studies documenting

the direct effects of these events on Indigenous Peoples are rare. In Mexico, respiratory diseases are almost twice as common among

Indigenous Peoples compared to non-Indigenous Peoples (de Leon-Martinez etal., 2020). In Alaska and northern Canada, alarming levels

of respiratory stress and disease have been reported among Inuit and First Nation communities in relation to wildﬁres (Howard etal.,

2021), as well as increased mould in houses due to ﬂooding resulting from increased precipitation (Furgal and Seguin, 2006; Harper etal.,

2015; Norton-Smith etal., 2016). Climate- and housing-related respiratory stress is also a risk factor for severe COVID-19 infection, which

has been highlighted in recent literature from an indigenous health perspective (de Leon-Martinez etal., 2020).

Secondary effects relate to ecosystem changes, for example, the increased risk of the acute spread of air-, soil-, vector-, food-, and

waterborne infectious diseases (Hueffer etal., 2019). Higher proportions of climate-related infectious diseases are reported among

indigenous groups compared to their non-indigenous neighbours, with examples from Torres Strait, Australia, showing a greater proportion

of tuberculosis, dengue, Ross River virus, melioidosis, and non-tuberculous mycobacterial infections (Hall etal., 2021) and in the Republic

of Sakha, Russia, high levels of zoonoses (Huber etal., 2020a). Increasing levels of livestock and canine diseases are also reported (Mamo,

2020; Bogdanova etal., 2021; Hillier etal., 2021). Another secondary health effect is an increase in human–animal conﬂicts, for example

1055

Health, Wellbeing and the Changing Structure of Communities Chapter 7

human–elephant conﬂicts in Namibia due to plant food scarcity (Mamo, 2020), human–bear conﬂicts in Arctic regions within Canada

(Wilder etal., 2017), human–tiger conﬂicts in Bangladesh (Haque etal., 2015) and increased predatory pressure on Indigenous Peoples’

livestock and game worldwide (Haque etal., 2015; Jaakkola etal., 2018; Mukeka etal., 2019; Mamo, 2020; Terekhina etal., 2021).

Undernutrition and metabolic disturbances associated with overnutrition and obesity due to the decreased availability or safety of local

and traditional foods and increased dependency on imported substitutes affect many Indigenous Peoples worldwide (Amstislavski etal.,

2013; Zavaleta etal., 2018; Houde etal., 2020; Jones etal., 2020; Akande etal., 2021; Bogdanova etal., 2021; Bryson etal., 2021) and are

especially severe for pregnant women and small children (Mamo, 2020; Olson and Metz, 2020; Bryson etal., 2021); these are ampliﬁed

by the combination of warming and the COVID-19 situation (Zavaleta-Cortijo etal., 2020). Decreased access to wild plants and animals

as food sources and medicine due to climate change is another threat to the health and wellness of indigenous communities (Greenwood

and Lindsay, 2019; Mamo, 2020; CIAT and and, 2021; Rankoana, 2021; Teixidor-Toneu etal., 2021).

Tertiary effects relate to culture-wide changes, for example, all forms of malnutrition due to climate-driven changes in food systems

and anxiety, mental illness and suicidal thoughts related to cultural and spiritual losses. A wide range of tertiary, culture-related effects

of climate change have been documented for Indigenous Peoples. These include anxiety, distress and other mental health impacts due

to direct and indirect processes of dispossession of land and culture related to the combination of climate change and other factors

(Richmond and Ross, 2009; Bowles, 2015; Norton-Smith etal., 2016; Jaakkola etal., 2018; Fuentes etal., 2020; Mamo, 2020; Middleton

etal., 2020b; Middleton etal., 2020a; Olson and Metz, 2020; Timlin etal., 2021). Increased risks of conﬂict and abuse, including violence

and homicide against females, and/or conﬂicts resulting from environmental activism, are other tertiary health threats for Indigenous

Peoples (Mamo, 2020). Between 2017 and 2019, close to 500 indigenous people were killed for activism in 19 different countries (Mamo,

2020). In Uganda, climate change drives indigenous men to increase their distance and time from home and their families in search of

water and food, leading to an increase in sexual violence against indigenous women and girls in their communities (Mamo, 2020).

Gender inequities amplify the tertiary health effects of climate change (Williams, 2018; Garnier etal., 2020). In an Inuit community, for

instance, women reported a higher level of mental stress related to climate change than men (Harper etal., 2015). Adverse pregnancy

outcomes and altered developmental trajectories have also been associated with climate change (Hall etal., 2021). Indigenous Batwa

women in Uganda reported experiencing more severe circumstances of food insecurity during pregnancy due to drought and unpredictable

seasons negatively impacting agricultural practices (de Leon-Martinez etal., 2020). More studies with a gender perspective on climate

change as a determinant of Indigenous Peoples’ health are needed, along with the perspectives of indigenous children and youth,

displaced individuals and communities in urban settings (Kowalczewski and Klein, 2018).

Because cultural continuity is a recognised health factor (Lemelin etal., 2010; de Leon-Martinez etal., 2020; Middleton etal., 2020b),

displaced Indigenous Peoples may suffer from climate change by worrying about impacts on non-displaced relatives and family and

from traditional food staples turning into expensive commodiﬁed products. This is a knowledge gap with lasting implications not only

on physical environments (Guo etal., 2018). Social connections and knowledge pathways are disrupted, leading to a decreased ability to

share locally harvested and cultivated foods (King and Furgal, 2014; Neufeld etal., 2020).

Tertiary effects of climate change on Indigenous Peoples’ health are primarily described in smaller case studies and not designed in a

way allowing for systematic international comparisons, which represents an important and signiﬁcant gap in our understanding of these

often-complex associations and impacts (Middleton etal., 2020b).

Future Risks for Indigenous People’s Health and Well-Being in a Changing Climate

Future risks for Indigenous Peoples’ health and well-being in a changing climate will result foremost from exacerbations of observed

impacts. Primary and secondary health risks are expected to increase as the frequency and/or severity of climate hazards grow in many

regions. As one example, melting permafrost in the Siberian Arctic is projected to lead to more outbreaks of anthrax (Bogdanova etal.,

2021). Tertiary health threats are expected to persist even with strong global initiatives to mitigate greenhouse gases (GHGs) (Butler and

Harley, 2010). Climate change is expected to compound non-climatic processes that lead to social exclusion and land dispossession that

underlay health inequalities experienced by Indigenous Peoples (Huber etal., 2020a).

Options and Opportunities for Reducing Future Risks and Building Capacity/Resilience for Indigenous Peoples’ Health and

Well-Being

Indigenous organisations worldwide stress the importance of applying a rights-based approach in responding to climate change (Mamo,

2020). Although Indigenous Peoples are often identiﬁed as being vulnerable to climate change, this framing does not always reﬂect the

diverse responses and adaptations of Indigenous Peoples to these ongoing challenges (Nursey-Bray etal., 2020). An emerging body of

Box7.1 (continued)

1056

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

research is focusing on the strength and resilience of indigenous communities globally as they adapt to these complex changes (Whyte,

2018; FAO etal., 2021).

During droughts and water shortages, for example, indigenous pastoralists may face additional challenges if water supply assistance

provides only for human needs and neglects water requirements of livestock (Mamo, 2020). Indigenous knowledge on how to adapt to

drought through storing and sharing strategies, for example, is valuable (Fatehpanah etal., 2020; Mamo, 2020).

Indigenous Peoples have been adapting to changes in their environments since time immemorial by developing new practices and

techniques (FAO et al, 2021). Their beliefs, value systems and principles include core elements and common values such as reciprocity,

solidarity, co-responsibility and community that are expressed in the dynamism of their knowledge systems (Lewis etal., 2020; Schramm

etal., 2020b). The relevance of these knowledge systems, which are holistic and tied to relationships between all living things, cannot be

ignored at this critical time (Garnier etal., 2020).

The health and equity impacts of climate change for Indigenous Peoples make mitigation efforts critical (Jones etal., 2020), including

policies and actions that consider the effects of colonisation. Colonisation constrains the design and diversity of potential climate

and health responses through its historic and ongoing suppression of Indigenous knowledge systems that are critical in supporting

community-led actions to reduce future risks (Billiot etal., 2019; Reid etal., 2019; Nursey-Bray etal., 2020).

Four Brief Case Studies to Illustrate the Innovativeness of Indigenous Peoples’ Adaptation to Climate Risks

Bedouin Pastoralists’ Grazing Practices Decrease the Risk of Wildﬁres in Israel and Increase Food Sovereignty

Wildﬁres are a main cause of deforestation in Israel, and in recent years climate stress has decreased the forest resilience to ﬁres (Klein

etal., 2019). The original landscape, a shrubland or maquis consisting mostly of oak and Pistacia, has been used since time immemorial

as grazing land for goats, sheep and camels belonging to Indigenous Bedouin people (Degen and El-Meccawi, 2009). Competing land use

has reshaped the landscape with pine monocultures and cattle farming, reducing the availability of land suitable for herding goats the

indigenous way (Perevolotsky and Sheffer, 2011). In addition, since 1950, plant protection legislation has decreased Bedouin forest

pastoralism in Israel by deﬁning indigenous black goats as an environmental threat (FAOLEX, 2021). In nature reserves where no human

interference has been allowed, these areas have regenerated into herbaceous shrublands susceptible to wildﬁres (Turco etal., 2017).

Meanwhile, urbanised Bedouin exist on lower incomes and experience higher levels of unemployment compared to other citizens, and

some keep non-pastoralised livestock in cities as a strategy for food sovereignty (Degen and El-Meccawi, 2009). In 2019, many severe

wildﬁres occurred in Israel due to extreme heatwaves and, in response, plant protection legislation was repealed, allowing Bedouin

pastoralists to graze their goats in areas from which they had been excluded. The amount of combustible undergrowth subsequently

decreased, reducing the risk for wildﬁre and their related impacts, while simultaneously facilitating indigenous food sovereignty among

the Bedouin (Mamo, 2020).

Gardening in the Ashes of Wildﬁres in the Paciﬁc Northwest as a Strategy to Decrease Food Insecurity and Increase Connections With the Land

In the central interior of what is now known as British Columbia (BC), 2017 was an especially severe wildﬁre season, with over 1.3million

hectares of land burned and 65,000 people displaced (Timler and Sandy, 2020). The unceded and ancestral lands of the Tsilhqot’in,

Dakelh and Secwépemc were impacted by two of the largest ﬁres (Verhaeghe etal., 2017). Communities affected by the BC wildﬁres

subsequently started indigenous gardens closer to home, to protect medicine and food plants and thereby sustaining relationships with

these plants, the land and the community (Timler and Sandy, 2020). As there are cultural teachings for ﬁre to cleanse the territory and

the land, community members and plants previously isolated became better connected because of the wildﬁres. The regrowth of plants is

part of the healing relationship between plants, people and other animals (Timler and Sandy, 2020). The wildﬁres were seen as events to

catalyse action and emphasise the importance of relationships to support foodways and gardening as responsibility.

Widening our understanding of gardening in the face of climate change and colonialism can support health and healing for Indigenous

and non-Indigenous Peoples. Gardening as a means of indigenous food sovereignty has long been utilised by a variety of indigenous

groups within Canada and elsewhere to address circumstances of chronic food insecurity and support health and wellness (Johnson-

Jennings etal., 2020; Timler and Sandy, 2020). The concept of gardening as both a Euro-Western agricultural practice and indigenous

practice encourages an increased reverence and connection with the land and wider engagement with the natural world (Whyte, 2018).

Much of this is because Indigenous knowledge and land management practices encompass processes that are known to be synergistic

Box7.1 (continued)

1057

Health, Wellbeing and the Changing Structure of Communities Chapter 7

and sustainable (Ottenhoff, 2021). Indigenous worldviews offer a different perspective on social resilience to environmental change, one

that is based on moral relationships of responsibility that connect humans to animals, plants and habitats (Grey and Patel, 2015). These

responsible practices not only ensure ecosystems are maintained for future generations; they centre the moral qualities necessary to carry

out the responsibilities of consent, reciprocity and trust. Moral qualities of responsibility are the foundation for relying on each other

when facing environmental challenges (Whyte, 2018; Miltenburg etal., 2021).

To restore these sustainable relationships, a resurgence is needed of community roles and responsibilities (Cidro etal., 2015) as well as

a reconsideration of the concept of food security and the role of gardening within diverse indigenous contexts. Offering individual or

community gardening as a solution to ‘food insecurity’, a Eurocentric measure of health, ignores colonial contexts and sovereignty

(Borrows, 2019; Timler and Sandy, 2020). Indigenous communities have historic, ongoing and evolving gardening and food gathering

practices, including a wide variety of land-based and aquatic foods (Turner and Turner, 2008; Mt. Pleasant, 2016). Euro-Western science

is beginning to recognise these longstanding relationships (Kamal etal., 2015; Hatﬁeld etal., 2018; Timler and Sandy, 2020). For many

indigenous communities, reconnecting with ancestral foodways holds the potential not only to address food security but to provide the

community cohesion, self-esteem and wellness (Gordon etal., 2018).

A New Food Composition Database in Uganda to Guide Local Policy in Healthy Eating Based on Indigenous Foods

In sub-Saharan Africa, climate change is an emerging risk factor for undernutrition, particularly in countries that rely on subsistence

agriculture (Sorgho etal., 2020). In Uganda, negative health effects associated with climate change are being observed, including

increased rates of food insecurity, with the highest rates recorded among the Batwa of Kanungu District, where 97% of households are

severely food insecure (Patterson etal., 2017). For many Indigenous Peoples, food security in a changing climate is a growing concern

(Guyot etal., 2006; Patterson etal., 2017). Locally harvested indigenous foods have been adversely impacted by climate change, while

connection to land is being disrupted by the processes of colonisation, discrimination and lack of representation in decision-making

groups, thereby restricting adaptive capacity for indigenous communities (Bryson etal., 2021). In Uganda, the Indigenous Batwa have

experienced signiﬁcant disparities resulting from the forced eviction from their territory, dispossessing them of their land and the ability

to provide indigenous foods to their families (Patterson etal., 2017; Scarpa etal., 2021).

Nutrient-speciﬁc knowledge of indigenous foods is limited among many communities in Africa. A new food composition database in

Uganda was constructed in dialogue with knowledge keepers from the Batwa and Bakiga Peoples to assess the nutrient density of these

locally harvested foods (Scarpa etal., 2021). As in other lower resource settings, no food composition tables are available for southwestern

Uganda. The only existing food database was designed for central and eastern Uganda; it does not include common recipes and local

foods consumed by Batwa and Bakiga communities (Scarpa etal., 2021). Using a community-based approach and collaboration with

local nutritionists, a list of foods was collected through focus group discussions, an individual dietary survey and market assessments.

Including these locally familiar foods ultimately supports a focus on indigenous justice and the importance of valuing indigenous food

systems and practices, which in many contexts have been found to have superior nutritional and environmental beneﬁts for communities

(Kuhnlein etal., 2013; Scarpa etal., 2021). This new and unique database including indigenous foods will not only guide local nutrition

and health initiatives, but also contribute towards policies related to indigenous food sovereignty and resilience to climate change.

Decreased Fragmentation of Winter Grazing Increases Mental and Spiritual Well-Being in Reindeer Herding Sámi and Decreases their

Dependency on Fossil Fuels

Sami are the Indigenous Peoples of northernmost Scandinavia and the Kola Peninsula of Russia, whose livelihoods have been traditionally

sustained by reindeer herding, hunting, ﬁshing and small-scale farming (Nilsson etal., 2011). Climate change is threatening core conditions

for reindeer herding, with Sami pastoralists describing the situation as ‘facing the limit of resilience’ (Furberg etal., 2011). Sami pastoralists

stress that an ability to continue reindeer herding is a prerequisite for their mental and spiritual health (Jaakkola etal., 2018).

In a pilot project for climate adaptation of reindeer herding run by the Swedish Sami Parliament, reindeer herding management plans (in

Swedish, renbruksplaner) were used as a tool to develop strategies for climate adaptation (Walkepää, 2019). Four Sami reindeer herding

cooperatives participated in the pilot study. They all agreed that climate change means that grazing patterns need to change. Traditionally,

mountain reindeer graze in the Scandinavian mountains close to Norway in summer and in the coastal areas close to the Gulf of Bothnia

in winter, representing a total migration route of up to 400 kilometres one-way. Rising temperatures are causing spring to occur earlier

in the coastal winter grazing land before the calving areas in the summer land are suitable for grazing and free from snow. When the

snow cover disappears, the herds are dispersed, so it is important to migrate while snow is still present (Walkepää, 2019). Migration

Box7.1 (continued)

1058

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

routes are being destabilised by weaker ice cover on water and by hazardous weather events. Competing land use due to infrastructure,

extractive industries, tourism, and energy production makes it difﬁcult to ﬁnd alternative grazing land. Supplementary feeding and

increased use of trucks to transport reindeer is one result. Herds that are dispersed due to bad snow conditions have an increased

exposure to predators (Walkepää, 2019; Uboni etal., 2020). By working strategically to secure adequate winter grazing and reduce

fragmentation of grazing areas more generally represents win-win strategies for achieving decreased mental stress levels while reducing

herders’ consumption of fossil fuels (Walkepää, 2019).

Box7.1 (continued)

7.1.7.3.7 Vulnerability Experienced through Food Systems

Stresses and shocks associated with climate change are drivers of food

insecurity, particularly in sub-Saharan Africa, Asia and Latin America

(Betts etal., 2018). The most vulnerable groups include smallholder

farmers, pastoralists, agricultural laborers, poorer households, refugees,

indigenous groups, women, children, the elderly and those who are

socioeconomically marginalised (FAO etal., 2018; IPCC, 2019b) (high

conﬁdence). Men, women, children, the elderly and the chronically

ill have different nutritional needs and these vulnerabilities may be

ampliﬁed by gendered norms and differential access to resources,

information and power (IPCC, 2019b). Extreme climate events have

immediate and long-term impacts on food insecurity and malnutrition

in poor and vulnerable communities, including when women and girls

need to undertake additional duties as laborers and caregivers (FAO

etal., 2018).

7.1.7.3.8 Health Vulnerability Experienced through Water and

Sanitation Systems

Water and sanitation systems are particularly vulnerable to extreme

weather events, and damage to such systems can lead to contamination

of drinking water and subsequent adverse health impacts (Howard

etal., 2016; Khan etal., 2015; Sherpa etal., 2014). In areas with only

very simple traditional excreta disposal facilities (e.g., latrines) and

traditional sources of water (e.g., unprotected wells), the repeated

Structure of chapter 07

Section 7.2Section 7.3

ADAPTATION &

CLIMATE RESILIENT

DEVELOPMENT PATHWAYS

(CRDP)

Health sector

Other sectors

Governance

OBSERVED IMPACTS



Health



Wellbeing



Migration



Conflict

PROJECTED

FUTURE RISKS

Section 7.4

Hazard

Section 7.1

FUTURE EMISSIONS

(RCPs)

2.6

4.5

8.5

Challenges for adaptation

FUTURE SOCIO-ECONOMIC

& DEMOGRAPHIC PATHWAYS

(SSPs)

SSP1

SSP3

SSP2

SSP4

SSP5

Challenges for mitigation

Vulnerability

Exposure

Hazard

Risk

Figure7.3 | Structure of the chapter following a pathway from hazard, exposure and vulnerability to the observed impacts, projected future risks, adaptation

and climate resilient development pathways.

1059

Health, Wellbeing and the Changing Structure of Communities Chapter 7

occurrence of ﬂoods and other extreme events can negatively affect

water quality at household and community levels and increase the

burden of food- and waterborne diseases (Cissé etal., 2016; Khan

etal., 2015; Kostyla etal., 2015).

7.1.8 Visual Guide to this Chapter

Figure7.3 provides a visual guide to this chapter. Section7.1 has brieﬂy

summarised major global frameworks and highlights groups that exhibit

heightened vulnerability and exposure to the climatic risks assessed in

this chapter. Section7.2 assesses observed impacts on health and well-

being, migration and conﬂicts that have emerged from interactions of

climate and weather-related hazards, exposure to such hazards and

vulnerability of communities and systems, while Section7.3 assesses

projected future risks. Section 7.4 assesses adaptation responses to

climate risks, opportunities for transformative change, co-beneﬁts

and how solutions for reducing climate impacts on health, well-being,

migration and conﬂicts may form part of the wider CRDPs.

7.2. Observed Impacts of Climate Change on

Health, Well-Being, Migration and Conﬂict

7.2.1 Observed Impacts on Health and Well-Being

Eleven categories of diseases and health outcomes have been identiﬁed

in this assessment as being climate-sensitive through direct pathways

(e.g., heat and ﬂoods) and indirect pathways mediated through natural

and human systems and economic and social disruptions (e.g., disease

vectors, allergens, air and water pollution, and food system disruption)

(high conﬁdence). A key challenge in quantifying the speciﬁc

relationship between climate and health outcomes is distinguishing

the extent to which observed changes in prevalence of a climate-

sensitive disease or condition are attributable directly or indirectly to

climatic factors as opposed to other non-climatic causal factors (Ebi

etal., 2020). A subsequent challenge is then determining the extent

to which those observed changes in health outcomes associated

with climate are attributable to events or conditions associated with

natural climate variability compared to persistent human induced

shifts in the mean and/or the variability characteristics of climate

(i.e., anthropogenic climate change). The context within which the

impacts of climate change affect health outcomes and health systems

is described in this chapter as being a function of risk, which is in turn

a product of interactions between hazard, exposure and vulnerability

(Chapter 1), with the impacts in turn having the potential to reinforce

vulnerability and/or exposure to risk (Figure7.4).

Interactions between hazard, exposure and vulnerability

Vulnerability

Exposure

Hazard

Risk

Age

Gender

Mobility

Access to care

Socio-economic status

Pre-existing conditions

Characteristics of health system

Outdoor employment

Housing quality

Location/local geography

Livelihood type

Heat, drought

Floods

Storms

Vector spread

CLIMATE SENSITIVE OUTCOMES

- Adverse health (VBD, WBD, FBD, infectious disease,

heat-related illness, mental health, under nutrition)

- Migration & displacement

- Conflicts

SYSTEM IMPACTS

Health system (patient loads, emergency responses, costs)

- Food systems

- Livelihood systems

IMPACTS

Figure7.4 | Interactions between hazard, exposure and vulnerability that generate impacts on health systems and outcomes, with selected examples.

WBD: waterborne disease, VBD: Vector-borne disease, and FBD: Food-borne disease.

1060

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Box7.2 | The Global Burden of Climate-Sensitive Health Outcomes Assessed in this Chapter

Global statistics for death and loss of health are increasingly described in terms of burden, which describes gaps between a population’s

actual health status and what its status would be if its members lived free of disease and disability to their collective life expectancy

(Shaffer etal., 2019). Burden for each disease/health outcome is estimated by adding together the number of years of life lost (YLL) by

a person because of early death and the number of years of life lived with disability (YLD) from the considered outcome. The resulting

statistic, the disability-adjusted life year (DALY) represents the loss of one year of life lived in full health. The total global burden of

disease (Vos etal., 2020), expressed in DALYs, is what the world’s health systems must manage and is reported annually in Global Burden

of Disease Study (Vos etal., 2020). The estimated current global burden of climate-sensitive diseases and conditions described in this

chapter, and the geographical regions most affected, are summarised in Table Box7.2.1. As was observed in Chapter 11 (‘Human Health’)

of AR5, the ‘background climate-related disease burden of a population is often the best single indicator of vulnerability to climate

change – doubling of risk of disease in a low disease population has much less absolute impact than doubling of the disease when the

background rate is high.’

The global magnitude of climate-sensitive diseases was estimated in 2019 to be 39,503,684 deaths (69.9% of total annual deaths) and

1,530,630,442 DALYs (Vos etal., 2020). Of these, cardiovascular diseases (CVDs) comprised the largest proportion of climate-sensitive

diseases (32.8% of deaths and 15.5% DALYs). The next largest category consists of respiratory diseases – with chronic respiratory disease

contributing to 7% of deaths and 4.1% of DALYs and respiratory infection and tuberculosis contributing to 6.5% of deaths and 6% of

DALYs. The observed trend of climate-sensitive disease deaths since 1990 is marked by increasing cardiovascular mortality and decreasing

mortality from respiratory infections, enteric diseases and other infectious diseases (Vos etal., 2020). Figure Box7.2.1 illustrates speciﬁc

global trends between 1990 and 2017 of selected health outcomes estimated by GBDs (Ahmad Kiadaliri etal. 2018).

TableBox7.2.1 | Global burden of climate-sensitive health risks assessed in this chapter (in order of assessment) (Vos etal., 2020) and synthesis of major observed

and projected impacts in most affected regions. Blue represents an increase in positive health impacts, green represents an increase in negative health impacts and yellow

represents an increase in both positive and negative impacts, but not necessarily in equal proportions. The conﬁdence level refers to both the attributed observed and

projected changes to climate change. No assessment means the evidence is insufﬁcient for assessment.

Data from Global Burden of Disease 2019

(Vos etal. 2020)

Chapter 7 Assessment

Health outcome (disease/

condition)

Global annual deaths

Regions most

affected (deaths)

Climate change observed

impacts

Climate change

projected impacts in

most affected regions

Selected key

references of the

Assessment

Malaria 643,381 Africa (92%) **** ***

M’Bra etal. (2018);

Caminade etal. (2019);

Gibb etal. (2020);

Tompkins and Caporaso

(2016b); Ebi etal.

(2021a)

Dengue 36,055 Asia (96%) *** ***

Bhatt etal. (2013);

Rocklöv & Dubrow

(2020); Messina etal.

(2019); Monaghan etal.

(2018)

Diarrhoeal diseases 1,534,443 Asia (56%) *** **

Cissé (2019); Levy etal.

(2018); Lo Iacono etal.

(2017); Carlton etal.

(2016)

Salmonella 79,046 Africa (89%) *** **

Cissé (2019); Smith and

Fazil (2019); Lake (2017)

RTIs 2,493,200 Asia (47%) **

Geier etal. (2018);

Oluwole (2017)

Non-communicable

respiratory illness

3,741,705 Asia (74%) *** **

Schweitzer etal. (2018);

Hansel etal. (2016);

Collaco etal. (2018);

D’Amato etal. (2020);

Silva etal. (2017);

Doherty etal. (2017);

Beggs (2021)

1061

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Data from Global Burden of Disease 2019

(Vos etal. 2020)

Chapter 7 Assessment

CVD 18,562,510 Asia (58%) ** ***

Stewart etal. (2017);

Phung (2016); Sun

(2018); Wang (2016);

Tian (2019); Chen (2019);

Zhang (2018)

Death from malignant

neoplasms

10,079,637 Asia (55%) ***

Ahmed etal. (2014);

Modenese etal. (2018);

Prueksapanich etal.

(2018)

Diabetes 1,551,170 Asia (56%) ** **

Hajat etal. (2017); Xu

etal. (2019b); Li etal.

(2014);Yang etal. (2016);

Velez-Valle etal. (2016);

Quast and Feng (2019)

Environmental heat and

cold exposure

47,461 Asia (46%) *** ****

Zhang etal. (2019b);

Green etal. (2019);

Murray etal. (2020); Ma

and Yuan (2021); Jones

etal. (2018); Russo etal.

(2019); Gosling etal.

(2017)

Nutritional deﬁciencies 251,577 Africa (43%) *** ***

Mbow etal. (2019); Lloyd

(2018); Springmann

etal. (2016b); Zhu etal.

(2018); Weyant etal.

(2018)

Mental healtha n.a. n.a. **** ****

Cianconi etal. (2020);

Charlson etal. (2021);

Hayes and Poland

(2018); Hrabok etal.

(2020); Obradovich etal.

(2018)

Legend

Climate change impacts Conﬁdence

Positive health impacts Very high ****

Negative health impacts High ***

Positive and negative impacts Medium **

No assessment Low *

Notes:

(a) Mental health data were not available (n.a.) due to lack of information in GBD 2019 related to annual deaths and the most affected regions.

Box7.2 (contnued)

1062

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Global trends of selected health outcomes estimated by the Global Burden of Disease Study

2.0

100

1990 1999 2008 2017

150

100

Diarrhoeal disease

Malaria

1990 1999 2008 2017

Forces of nature

Protein-energy malnutrition

1990 1999 2008 2017

2.0

1.5

1.0

0.1

Dengue

Heat and cold exposure

Deaths per 100,000 people Deaths per 100,000 people

All causes

1400

1200

1000

800

600

1990 1999 2008 2017

African region

Region of the Americas

Eastern Mediterranean region

European region

South-East Asia region

Western Pacific region

FigureBox7.2.1 | Global trends of selected health outcomes estimated by GBDs. Source: Ahmad Kiadaliri etal. (2018a).

Box7.2 (contnued)

7.2.2 Observed Impacts on Communicable Diseases

7.2.2.1 Observed Impacts on Vector-Borne Diseases

Climate-sensitive VBDs include mosquito-borne diseases, rodent-borne

diseases and tick-borne diseases. Many infectious agents, vectors, non-

human reservoir hosts, and pathogen replication rates can be sensitive to

ambient climatic conditions. Elevated proliferation and reproduction rates

at higher temperatures, longer transmission season, changes in ecology

and climate-related migration of vectors, reservoir hosts or human

populations contribute to this climate sensitivity (Rocklöv & Dubrow,

2020; Semenza and Paz, 2021). Age-standardised DALY rates for many

VBDs have decreased over the last decade due to factors unrelated to

climate. Vulnerability to VBD is strongly determined by sociodemographic

factors (e.g., children, the elderly and pregnant women are at greater

risk) with exposure to vectors being strongly inﬂuenced by various

factors including socioeconomic status, housing quality, healthcare

access, susceptibility, occupational setting, recreational activity, conﬂicts

and displacement ( Rocklöv & Dubrow, 2020; Semenza and Paz, 2021).

Figure7.5 illustrates how climatic and non-climatic drivers and responses

determine VBD outcomes.

Evidence has increased since AR5 that the vectorial capacity has

increased for dengue fever, malaria and other mosquito-borne

diseases and that higher global average temperatures are making

wider geographic areas more suitable for transmission (very high

conﬁdence). Transmission rates of malaria are directly inﬂuenced by

climatic and weather variables such as temperature, with non-climatic

socioeconomic factors and health system responses counteracting

the climatic drivers (very high conﬁdence). The burden of malaria is

greatest in Africa, where more than 90% of all malaria-related deaths

occur (M’Bra etal., 2018; Caminade etal., 2019). Between 2007 and

2017, DALYs for malaria have decreased by 39% globally. Malaria

is mainly caused by ﬁve distinct species of plasmodium parasite

(Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae,

Plasmodium ovale and Plasmodium knowlesi) and is transmitted by

Anopheline mosquitoes. Evidence suggests that in highland areas of

Colombia and Ethiopia, malaria has shifted in warmer years towards

higher altitudes, indicating that, without intervention, malaria will

increase at higher elevations as the climate warms (Siraj etal., 2014;

Midekisa etal., 2015). Each year, local outbreaks of malaria occur due

to importation in areas from which it was once eradicated, such as

Europe, but the risk of re-establishment is considered low.

1063

Health, Wellbeing and the Changing Structure of Communities Chapter 7

The transmission of dengue fever is linked to climatic and weather

variables such as temperature, relative humidity and rainfall (high

conﬁdence). The dengue virus is carried and spread by Aedes

mosquitoes, primarily Aedes aegypti. Dengue has the second highest

burden of VBDs, with the majority of deaths occurring in Asia (Bhatt

et al., 2013). Since 1950, global dengue burden has grown and is

attributable to a combination of climate-associated expansion in the

geographic range of the vector species and non-climatic factors such

as globalised air trafﬁc, urbanisation and ineffective vector abatement

measures. Temperature, relative humidity and rainfall variables are

signiﬁcantly and positively associated with increased dengue case

incidence and/or transmission rates globally, including in Vietnam

(Phung etal., 2015; Xuan le etal., 2014), Thailand (Xu etal., 2019a),

India (Mutheneni etal., 2017; Rao etal., 2018; Mala and Jat, 2019),

Indonesia (Kesetyaningsih etal., 2018), the Philippines (Carvajal etal.,

2018), the USA (Lopez etal., 2018; Pena-Garcia etal., 2017; Duarte

etal., 2019; Rivas etal., 2018; Silva etal., 2016a), Jordan (Obaidat

and Roess, 2018) and Timor-Leste (Wangdi etal., 2018). Variation in

winds, sea surface temperatures and rain over the tropical eastern

Paciﬁc Ocean (El Niño-Southern Oscillation; ENSO) have been linked to

increased dengue incidence in Colombia (Quintero-Herrera etal., 2015;

McGregor and Ebi, 2018; Pramanik etal., 2020) and its interannual

variation successfully forecasted in Ecuador using ENSO indices

as predictors (Petrova etal., 2019). The observed time lag between

climate exposures and increased dengue incidence is approximately

1–2months (Chuang etal., 2017; Lai, 2018; Chang etal., 2018).

Changing climatic patterns are facilitating the spread of CHIKV, Zika,

Japanese encephalitis and Rift Valley Fever in Asia, Latin America,

North America and Europe (high conﬁdence). Climate change may

have facilitated the emergence of CHIKV as a signiﬁcant public health

challenge in some Latin American and Caribbean countries (Yactayo etal.,

2016; Pineda etal., 2016) and contributed to chikungunya outbreaks

in Europe (Rocklöv etal., 2019; Mascarenhas etal., 2018; Morens and

Fauci, 2014). The Zika virus outbreak in South America in 2016 was

preceded by 2007 outbreaks on Paciﬁc islands and followed a period

of record high temperatures and severe drought conditions in 2015

(Paz and Semenza, 2016; Tesla etal., 2018). Increased use of household

water storage containers during the drought is correlated with a range

expansion of Aedes aegypti during this period, increasing household

exposure to the vector (Paz and Semenza, 2016). Changing climate also

appears to be a risk factor for the spread of Japanese encephalitis to

higher altitudes in Nepal (Ghimire and Dhakal, 2015) and in southwest

China (Zhao etal., 2014). In eastern Africa, climate change may be a risk

factor in the spread of Rift Valley Fever (Taylor etal., 2016a).

Changes in temperature, precipitation, and relative humidity have

been implicated as drivers of West Nile fever in southeastern Europe

(medium conﬁdence). The average temperature and precipitation prior

to the exceptional 2018 West Nile outbreak in Europe was above the

1981–2010 period average, which may have contributed to an early

upsurge of the vector population (Marini etal., 2020; Haussig etal.,

2018; Semenza and Paz, 2021). In 2019 and 2020, West Nile fever was

ﬁrst detected in birds and subsequently in humans in Germany and the

Netherlands (Ziegler etal., 2020; Vlaskamp etal., 2020).

Climate change has contributed to the spread of the Lyme disease

vector Ixodes scapularis, a corresponding increase in cases of Lyme

disease in North America (high conﬁdence) and the spread of the Lyme

disease and tick-borne encephalitis vector Ixodes ricinus in Europe

Analysis of the

underlying drivers of

infectious disease threat

events (IDTE)

detected in Europe during

2008–2013 by epidemic

intelligence at the European

Centre of Disease Prevention

and Control

Surveillance and

reporting failure

Occupational Prevention LifestyleMigration

Travel and

tourism

Global trade

Natural

environment

Human-made

environmentClimate

IDTE (epidemics or first

autochthonous cases) of

vector-borne diseases

Globalization and

environmental drivers

Public health systems drivers

Sociodemographic drivers

Size is proportional to the overall

requency of the driver

Figure7.5 | Analysis of the underlying drivers of infectious disease threat events (IDTEs) detected in Europe from 2008 to 2013 by epidemic intelligence

at the European Centre of Disease Prevention and Control. Seventeen drivers were identiﬁed and categorised into three groups: globalisation and environment (green),

sociodemographic (red) and public health system (blue). The drivers are illustrated as diamond shapes and arranged in the top and bottom row; the sizes are proportional to the

overall frequency of the driver. Here IDTEs (epidemics or ﬁrst autochthonous cases) of VBDs are illustrated as a horizontal row of dots in the middle. These empirical data include

the IDTEs of VBDs such as West Nile fever, malaria, dengue fever, chikungunya and Hantavirus infection. Source: Semenza etal. (2016).

1064

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

(medium conﬁdence). In Canada, there has been a geographic range

expansion of the black-legged tick I. scapularis, the main vector of

Borrelia burgdorferi, the agent of Lyme disease. Vector surveillance of I.

scapularis has identiﬁed strong correlation between temperatures and

the emergence of tick populations, their range and recent geographic

spread, with recent climate warming coinciding with a rapid increase

in human Lyme disease cases (Clow etal., 2017; Cheng etal., 2017;

Gasmi etal., 2017; Ebi etal., 2017). Ixodes ricinus, the primary vector

in Europe for both Lyme borreliosis and tick-borne encephalitis is

sensitive to humidity and temperature (Daniel etal., 2018; Estrada-

Peña and Fernández-Ruiz, 2020) (high conﬁdence). There has been an

observed range expansion to higher latitudes in Sweden and to higher

elevations in Austria and the Czech Republic.

Rodent-borne disease outbreaks have been linked to weather and

climate conditions in a small number of studies published since AR5,

but more research is needed in this area. In Kenya, a positive association

exists between precipitation patterns and Theileria-infected rodents,

but for Anaplasma, Theileria and Hepatozoon, the association between

rainfall and pathogen varies according to rural land use types (Young

etal., 2017). Weather variability plays a signiﬁcant role in transmission

rates of haemorrhagic fever with renal syndrome (HFRS) (Hansen etal.,

2015; Xiang etal., 2018; Liang etal., 2018; Fei etal., 2015; Xiao etal.,

2014; Vratnica etal., 2017; Roda Gracia etal., 2015; Monchatre-Leroy

etal., 2017; Bai etal., 2019). In Chongqing, HFRS incidence has been

positively associated with rodent density and rainfall (Bai etal., 2015).

7.2.2.2 Observed Impacts on Waterborne Diseases

Important waterborne diseases (WBDs) include diarrhoeal

diseases (such as cholera, shigella, cryptosporidiosis and typhoid),

schistosomiasis, leptospirosis, hepatitis A and E and poliomyelitis (Cisse,

2019; Houéménou etal., 2021; Hassan etal., 2021; Archer etal., 2020;

Mbereko etal., 2020; Fan etal., 2021). The number of cases of WBDs

is considerable, and even in high-income countries WBDs continue

to be a concern (Cissé etal., 2018; Kirtman etal., 2014; Levy etal.,

2018; Murphy etal., 2014; Brubacher etal., 2020; Lee etal., 2021).

Nevertheless, diarrhoea mortality has declined substantially since

1990, although there are variations by country, and the global burden

of WBDs has decreased in line with vaccination coverage of some

WBDs (such as polio and cholera), poverty reduction and improved

sanitation and hygiene (Jacob and Kazaura, 2021; Mutono etal., 2020;

Lee etal., 2019; Semenza and Paz, 2021; Jacob and Kazaura, 2021;

Mutono etal., 2020).

Drinking water containing pathogenic microorganisms is the main

driver of the burden of WBDs (Murphy etal., 2014; Lee etal., 2021;

Chen etal., 2021b; Musacchio etal., 2021). WBD outbreaks, particularly

intestinal diseases, are attributable to a combination of the presence

of particular pathogens (bacteria, protozoa, viruses or parasites) and

the characteristics of drinking water systems in a given location (Bless

etal., 2016; Ligon and Bartram, 2016; Mutono etal., 2021; Ferreira

etal., 2021).

Since AR5 there is a growing body of evidence that increases in

temperature (very high conﬁdence), heavy rainfall (high conﬁdence),

ﬂooding (medium conﬁdence) and drought (low conﬁdence) are

associated with an increase of diarrhoeal diseases. In the majority of

studies there is a signiﬁcant positive association observed between

WBDs and elevated temperatures, especially in areas where water,

sanitation and hygiene (WASH) deﬁciencies are signiﬁcant (Levy

etal., 2018; Carlton etal., 2016; Levy etal., 2018; Sherpa etal., 2014;

Guzman Herrador etal., 2015; Levy etal., 2016; Lo Iacono etal., 2017).

In Ethiopia, South Africa and Senegal, increases in temperatures are

associated with increases in diarrhoea, while in Ethiopia, Senegal and

Mozambique, increases in monthly rainfall are associated with an

increase in cases of childhood diarrhoea (Azage etal., 2015; Thiam

etal., 2017; Horn etal., 2018). Similar associations between weather

and diarrhoea have been observed in Cambodia, China, Bangladesh,

Paciﬁc Island countries and the Philippines (McIver et al., 2016a;

McIver etal., 2016b; Liu etal., 2018; Wu etal., 2014; Matsushita etal.,

2018). Heavy precipitation events have been consistently associated

with outbreaks of WBDs in Europe, USA, UK and Canada (Guzman

Herrador etal., 2015; Levy etal., 2016; Lo Iacono etal., 2017; Curriero

etal., 2001; Guzman Herrador etal., 2016; Levy etal., 2018; Semenza

and Paz, 2021).

Impacts of ﬂoods include outbreaks of WBDs, with such events

disproportionately affecting the young, elderly and immunocompromised

(Suk etal., 2020; Guzman Herrador etal., 2015; Levy etal., 2016; Lo

Iacono etal., 2017; Zhang etal., 2019a). Water shortage and drought

have been found associated with diarrhoeal disease peaks (Epstein

etal., 2020b; Subiros etal., 2019; Boithias etal., 2016), while some

reviews found insufﬁcient evidence of the effects of drought on

diarrhoea (Levy et al, 2016 ; Asmall etal., 2021; Epstein etal., 2020b;

Subiros etal., 2019; Boithias etal., 2016; Ramesh etal., 2016).

Heavy rainfall and higher than normal temperatures are associated

with increased cholera risk in affected regions (very high conﬁdence).

Cholera is an acute diarrhoeal disease typically caused by the

bacterium Vibrio cholerae that can result in severe morbidity and

mortality. Maximum and minimum temperatures and precipitation

have been negatively associated with cholera cases. Cholera outbreaks

have occurred in several regions after natural disasters, including

cholera incidence increasing three-fold in El Niño-sensitive regions of

Africa (Mpandeli etal., 2018; Amegah etal., 2016; Escobar etal., 2015;

Jutla etal., 2017; Asadgol etal., 2019; Moore etal., 2018; Moore etal.,

2017; Camacho etal., 2018; IPCC, 2019a; Cross-Chapter BoxILLNESS

in Chapter 2; Box3.3).

Heavy rainfall, warmer weather and drought are linked to increased

risks for other GI infections (high conﬁdence). As temperature

increases, bacterial causes of GI infection also appear to increase,

and this association is variably inﬂuenced by humidity and rainfall

(Ghazani etal., 2018; Levy, 2016). In New York it has been found that

every 1°C increase in temperature was correlated with a 0.70–0.96%

increase in daily hospitalisation for GI infections (Lin etal., 2016). In

the Philippines, leptospirosis and typhoid fever showed an increase

in incidence following heavy rainfall and ﬂooding events (Matsushita

etal., 2018).

1065

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Box7.3 | Cascading Risk Pathways Linking Waterborne Disease to Climate Hazards

The causal linkages between climate variability and change and incidence of WBDs follow multiple direct and indirect pathways, often as

part of a cascading series of risks (Semenza, 2020). For example, extreme precipitation can result in a cascading hazard or disease event

with implications of greater magnitude than the initial hazard, especially if there are pre-existing vulnerabilities in critical infrastructure

and human populations (Semenza and Paz, 2021). Intense or prolonged precipitation can ﬂush pathogens in the environment from

pastures and ﬁelds to groundwater, rivers and lakes, consequently inﬁltrating water treatment and distribution systems (Howard etal.,

2016; Khan etal., 2015; Sherpa etal., 2014; Cissé etal., 2016; Kostyla etal., 2015; Chapter 4). Table Box7.3.1 shows the variety and

complexity of pathways between climate hazard and WBD outcomes (Semenza, 2020).

TableBox7.3.1 | Pathways between climate hazard and waterborne disease (WBD) outcomes. Source: Semenza (2020).

Cascading risk pathways from heavy rain and ﬂooding

Storm runoff yields water turbidity, which compromises water treatment efﬁciency

Storm runoff and ﬂoods mobilise and transport pathogens

Overwhelmed or damaged infrastructure compromises water treatment efﬁciency

Floods overwhelm containment system and discharge untreated wastewater

Floods damage critical water supply and sanitation infrastructure

Floods displace populations towards inadequate sanitation infrastructure

Cascading risk pathways from drought

Low water availability augments travel distance to alternate (contaminated) sources

Intensiﬁed demand for and sharing (e.g., with livestock) of limited water resources decreases water availability and quality

Intermittent drinking water supply results in cross-connections with sewer lines and water contamination

Uncovered household water containers are a source of vector breeding

Poor hygiene due to decreased volume of source water and increased concentration of pathogens

Exposure to accumulated human excrements and animal manure

Cascading risk pathways from increasing temperature

Extended transmission season for opportunistic pathogens

Permissive temperature for the replication of marine bacteria

Enhanced pathogen load in animal reservoirs (e.g., chicken)

Pathogen survival and proliferation outside of host

Wildﬁres during heatwaves degrade water quality

Exposure to contaminated water due to higher water consumption

Behaviour change due to extended season (e.g., food spoilage during barbeque)

Cascading risk pathways from sea level rise

Population displacement due to powerful storm surges

Disruption of drinking water supply and sanitation infrastructure due to inundation

Decline in soil and water quality due to saline intrusion into coastal aquifers

Seawater inﬁltration into drinking water distribution and sewage lines

Notes:

Examples are purposely not exhaustive and should be considered illustrative.

7.2.2.3 Observed Impacts on Food-Borne Diseases

FBDs refer to any illness resulting from ingesting food that is spoiled

or contaminated by pathogenic bacteria, viruses, parasites, toxins,

pesticides and/or medicines (WHO, 2018b). FBD risks are present

throughout the food chain, from production to consumption, and most

often arise due to contamination at source and from improper food

handling, preparation and/or storage (Smith and Fazil, 2019; Semenza

and Paz, 2021). As with WBDs, FBD outbreaks can follow multiple

causal pathways as climatic risk factors interact with food production

and distribution systems, urbanisation and population growth,

resource and energy scarcity, decreasing agricultural productivity,

price volatility, modiﬁcation of diet trends, new technologies and the

emergence of antimicrobial resistance (Lake, 2018; Yeni and Alpas,

2017). The burden of FBDs is also linked to malnutrition as reduced

immunity increases susceptibility to various food-borne pathogens and

toxins (FAO, 2020).

A strong association exists between increases in FBDs and high air and

water temperatures and longer summer seasons (very high conﬁdence).

The risks occur through complex transmission pathways throughout

the food chain and the wide range of food-borne pathogens (Cisse,

2019; Hellberg and Chu, 2016; Lake and Barker, 2018; Park et al.,

2018b; Smith and Fazil, 2019). The food-borne pathogens of most

concern are those having low infective doses, a signiﬁcant persistence

in the environment and high stress tolerance to temperature change

1066

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

(e.g., enteric viruses, Campylobacter spp., Shiga toxin-producing E.

coli strains, Mycobacterium avium, tuberculosis complexes, parasitic

protozoa and Salmonella) (Lake, 2018; Lake, 2017; Lake and Barker,

2018; Smith and Fazil, 2019; European Food Safety Authority 2020;

Semenza and Paz, 2021). Priority risks include marine biotoxins,

mycotoxins, salmonellosis, vibriosis, transfer of contaminants due to

extreme precipitation, ﬂoods, increased use of chemicals in the food

chain (plant protection products, fertilizers, veterinary drugs) and

potential residues in food (European Food Safety Authority 2020;

World Health Organization 2018b).

There is a strong association observed between the increase in average

ambient temperature and increases in Salmonella infections (high

conﬁdence). Most types of Salmonella infections lead to salmonellosis,

while some other types (Salmonella Typhi and Salmonella Paratyphi)

can lead to typhoid fever or paratyphoid fever. The transmission to

humans of the non-typhoidal Salmonella infection, one of the most

widespread FBDs, usually occurs through eating foods contaminated

with animal faeces. Studies conducted in Australia (Milazzo et al.,

2016), New Zealand (Lal etal., 2016), the UK (Lake, 2017), South Korea

(Park etal., 2018a; Park etal., 2018c; Park etal., 2018a), Singapore (Aik

etal., 2018) and Hong Kong, SAR of China (Wang etal., 2018a; Wang

et al., 2018b), have shown that Salmonella outbreaks are strongly

associated with temperature increases.

Signiﬁcant associations exist between FBDs due to Campylobacter,

precipitation and temperature (medium conﬁdence). The timing of

heat-associated Campylobacteriosis events varies across countries,

with infection rates in the UK appearing to decline immediately

after periods of high rainfall (Djennad etal., 2019; Lake etal., 2019;

Rosenberg etal., 2018; Yun etal., 2016; Weisent etal., 2014). This

suggests the association with climate may be indirect and due to

weather conditions that encourage outdoor food preparation and

recreational activities (Lake, 2017; Semenza and Paz, 2021).

Outbreaks of human and animal Cryptococcus have been reported as

being associated with a combination of climatic factors and shifts in

host and vector populations (Chang and Chen, 2015; Rickerts, 2019). The

prevalence of childhood cryptosporidiosis, which is the second leading

cause of moderate to severe diarrhoea among infants in the tropics

and subtropics, shows associations with population density and rainfall,

with contamination due to Cryptosporidium spp. being 2.61 times

higher during and after heavy rain (Lal etal., 2019; Young etal., 2015;

Khalil etal., 2018). Studies from Ghana, Guinea Bissau, Tanzania, Kenya

and Zambia show a higher prevalence of Cryptosporidium during high

rainfall seasons, with some peaks observed before, at the onset or at

the end of the rainy season (Squire and Ryan, 2017).

7.2.2.4 Respiratory Tract Infections

Climatic risk factors for respiratory tract infections (RTIs) due to

multiple pathogens (bacteria, viruses and fungi) include temperature

and humidity extremes, dust storms, extreme precipitation events and

increased climate variability. Amongst a range of RTIs, pneumonia and

inﬂuenza represent a signiﬁcant disease burden (Ferreira-Coimbra

etal., 2020; Lafond etal., 2021; McAllister etal., 2019; Wang etal.,

2020c). The drivers of pneumonia incidence are complex and include a

range of possible non-climate as well as climate factors. For example,

chronic diseases (e.g., lung disease, chronic obstructive pulmonary

disease (COPD) and asthma), other comorbidities, a weak immune

system, age, gender, community, passive smoking, air pollution and

childhood immunisation may confound the climate pneumonia

relationship (Miyayo etal., 2021).

In temperate regions, the incidence of pneumonia is higher in winter

months, but the exact causes of this seasonality remain debated

(Mirsaeidi etal., 2016). With regards to temperature, various J-shaped,

U-shaped or V-shaped temperature–pneumonia relationships

have been reported in the literature (Huang etal., 2018; Kim etal.,

2016; Liu etal., 2014; Qiu etal., 2016; Sohn etal., 2019) with such

relationships dependent on location. Humidity also appears important

but, like temperature, its effect is not consistent across studies – low

temperatures and low humidity (Davis etal., 2016), high temperatures

and high humidity (Lam etal., 2020) and low temperatures and high

humidity (Miyayo etal., 2021) have all been found to be associated

with an increased incidence of pneumonia.

Day-to-day variations in temperature also appear important.

For Australia, increases in emergency room visits for childhood

pneumonia are associated with sharp temperature drops (Xu etal.,

2014). Large inter-daily changes in temperature are important for

respiratory disease incidence in Guangzhou, China (Lin etal., 2013)

and Shanghai (Lei etal., 2021) while rapidly changing and extreme

temperatures during pregnancy have been linked to childhood

pneumonia (Miao etal., 2017; Zeng etal., 2017; Zheng etal., 2021).

In tropical and subtropical areas of Africa and Asia, pneumonia

incidence has been reported to be higher during the rainy season,

pointing to a positive association between pneumonia patterns and

temperature and precipitation (Chowdhury et al., 2018a; Lim and

Siow, 2018; Paynter etal., 2010).

The degree to which the timing, duration and magnitude of local

inﬂuenza virus epidemics is dependent on climate factors is poorly

understood (Lam et al., 2020). Further, a host of non-climate

confounders are likely to inﬂuence the incidence of seasonal

inﬂuenza (Caini et al., 2018). This poses a number of challenges

for making reliable climate-based epidemiological forecasts for

inﬂuenza (Gandon etal., 2016). Although no association between

anomalous climate conditions and inﬂuenza have been reported in

some locations (Lam etal., 2020), generally, low winter temperatures

and humidity in temperate regions and periods of high humidity

and precipitation in the tropical and subtropical regions have been

linked to outbreaks of inﬂuenza (Deyle etal., 2016; Soebiyanto etal.,

2015; Tamerius et al., 2013). However, the climate sensitivity of

inﬂuenza may be more complex than this, with both high and low

humidity; the amount and intensity of precipitation; solar activity

and/or sunshine; and latitude also being important (Axelsen etal.,

2014; Chong etal., 2020b; Geier etal., 2018; Park etal., 2019; Qu,

2016; Smith et al., 2017; Wang etal., 2017c; Zhao et al., 2018a).

Moreover, the shape of the climate variable inﬂuenza relationship

may be conditioned on inﬂuenza type (Chong etal., 2020a). Further,

distinct periods of weather variability characterised by rapid inter-

daily changes in temperature may act as precursors to inﬂuenza

epidemics as has been demonstrated for the marked 2017–2018

1067

Health, Wellbeing and the Changing Structure of Communities Chapter 7

inﬂuenza season and others across the USA (Liu etal., 2020a; Zhao

etal., 2018a). For the eastern Mediterranean, such rapid weather

changes are associated with the ‘Cyprus Low’, with the timing and

magnitude of seasonal inﬂuenza related to the interannual frequency

of this particular weather regime (Hochman etal., 2021). Potentially,

large-scale modes of climatic variability such as ENSO and the Indian

Ocean Dipole, which strongly moderate the frequency of weather

regimes in some parts of the world, could affect inﬂuenza pandemic

dynamics. However, studies conducted to date report inconsistent

results. Some point to an increased (decreased) severity of seasonal

inﬂuenza during El Niño (La Niña) (Oluwole, 2015; Oluwole, 2017),

while others ﬁnd inﬂuenza to be more severe and frequent when

coinciding with La Niña events (Chun etal., 2019; Flahault etal.,

2016; Shaman and Lipsitch, 2013). This raises the possibly of non-

stationary associations between large-scale modes of climatic

variability and inﬂuenza dynamics (Onozuka and Hagihara, 2015)

as found for other diseases (Kreppel etal., 2014), something that

might be expected given El Niño’s time-varying impact on global

precipitation and temperature ﬁelds and associated impacts on

health outcomes (McGregor and Ebi, 2018).

Cross-Chapter BoxCOVID | COVID-19

Authors: Maarten van Aalst (Netherlands, Chapter 16), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Ayansina Ayanlade

(Nigeria, Chapter 9), Lea Berrang-Ford (United Kingdom/Canada, Chapter 16), Rachel Bezner Kerr (Canada/USA, Chapter 5), Robbert

Biesbroek (Netherlands, Chapter 13), Kathryn Bowen (Australia, Chapter 7), Martina Angela Caretta (Sweden, Chapter 4), So-Min Cheong

(Republic of Korea, Chapter 17), Winston Chow (Singapore, Chapter 6), Mark John Costello (New Zealand/Norway/Ireland, Chapter 11,

CCP1), Kristie Ebi (USA, Chapter 7), Elisabeth Gilmore (USA/Canada, Chapter 14), Bruce Glavovic (South Africa/New Zealand, Chapter

18, CCP2), Walter Leal (Germany, Chapter 8), Stefanie Langsdorf (Germany, TSU), Elena Lopez-Gunn (Spain/United Kingdom, Chapter 4),

Ruth Morgan (Australia, Chapter 4), Aditi Mukherji (India, Chapter 4), Camille Parmesan (France/ United Kingdom /USA, 2), Mark Pelling

(United Kingdom, Chapter 6), Elvira Poloczanska (United Kingdom, TSU), Marie-Fanny Racault (United Kingdom/France, Chapter 3), Diana

Reckien (Germany/Netherlands, Chapter 17), Jan C. Semenza (Sweden, Chapter 7), Pramod Kumar Singh (India, Chapter 18), Stavana E.

Strutz (USA), Maria Cristina Tirado von der Pahlen (Spain/USA, Chapter 7), Corinne Schuster-Wallace (Canada), Alistair Woodward (New

Zealand, Chapter 11), Zinta Zommers (Latvia, Chapter 17)

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, emerged in late 2019, halfway through the

preparation of the IPCC WGII Sixth Assessment Report. This Cross-Chapter Box assesses how the massive shock of the pandemic and

response measures interact with climate-related impacts and risks as well as its signiﬁcant implications for risk management and climate

resilient development.

COVID-19 and environmental connections

Infectious diseases may emerge and spread through multiple climate-related avenues, including direct effects of climatic conditions

on disease reproduction and transmission and various indirect effects, often interlinked with ecosystem degradation (high conﬁdence).

Climate change is affecting the risk of emerging infectious diseases by contributing to factors that drive the movements of species,

including vectors and reservoirs of diseases, into novel human populations and vice versa (high conﬁdence) (Sections2.4.2.7, 5.2.2.3;

Cross-Chapter Box Illness in Chapter 2; IPCC, 2019b; IPBES 2020). The spillover of some emerging infectious diseases from wildlife into

humans is associated with live animal–human markets, intensiﬁed livestock production and climate-related movements of humans

and wild animals into new areas that alter human–animal interactions (Section2.4.2.7; Chapter 3; Sections5.2.2.3, 7.2; Cross-Chapter

BoxILLNESS in Chapter 2; Cross-Chapter BoxMOVING PLATE in Chapter 5).

Human-to-human transmission is the prominent driver in the spread of the COVID-19 pandemic, rather than climatic drivers (high

conﬁdence). There is emerging literature on the environmental determinants of COVID-19 transmission, incidence and mortality rates,

with initial evidence suggesting that temperature, humidity and air pollution contribute to these patterns (Brunekreef etal., 2021; Xiong

etal., 2020; Zhang etal., 2020b; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and climate). Climate

change is altering environmental factors like temperature and seasonality that affect COVID-19 transmission (Choi etal., 2021).

The impact of COVID-19 containment measures resulted in a temporary reduction in greenhouse gas (GHG) emissions and reduced air

pollution (high conﬁdence) (IPCC WGI TS; Arias etal., 2021; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air

quality and climate). However, global and regional climate responses to the radiative effect were undetectable above internal climate

variability due to the temporary nature of emission reductions. They therefore do not result in detectable changes in impacts or risks due

to changes in climate hazards (Arias et al 2021; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and

climate; Naik etal., 2021).

1068

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Cascading and compounding risks and impacts

The COVID-19 pandemic posed a severe shock to many socioeconomic systems, resulting in substantial changes in vulnerability and

exposure of people to climate risks (high conﬁdence). The disease and response measures signiﬁcantly affected human health, economic

activity, food production and availability, health services, poverty, social and gender inequality, education, supply chains, infrastructure

maintenance and the environment. These COVID-19 impacts interact with many risks associated with climate change (IMF, 2020), often

through a cascade of impacts across numerous sectors (van den Hurk etal., 2020). Beyond COVID-19-related mortality and long-term

COVID, mortality from other diseases (some of which may also have a climate-related component), as well as maternal and neonatal

mortality, increased because of disruption in health services (Barach etal., 2020; Maringe etal., 2020; Zadnik etal., 2020; Goyal etal.,

2021). In addition, a rapid rise in poverty has disproportionately affected poorer countries and people (Ferreira etal., 2021), and thus

increased their vulnerability. After many years of steady declines, extreme poverty increased by about 100million people in 2020 (World

Bank, 2021). The effects of the pandemic increased food insecurity and malnourishment, which increased by 1.5percentage points to

around 9.9% in 2020 after being virtually unchanged for the previous ﬁve years (FAO etal., 2021).

During the pandemic, extreme weather and climate events such as droughts, storms, ﬂoods, wildﬁres and heatwaves continued, resulting

in disastrous compounding impacts (high conﬁdence). Between March and September 2020, 92 extreme weather events coincided with

the COVID-19 pandemic, affecting an estimated 51.6million people; additionally, 431.7million people were exposed to extreme heat,

and 2.3million people were affected by wildﬁres (Walton and van Aalst, 2020). The COVID-19 pandemic, in combination with extreme

events, affected disaster preparedness, disaster response and safe evacuations, while physical distancing regulations reduced the capacity

of temporary shelters (UN DRR Asia-Paciﬁc, 2020; Tozier de la Poterie etal., 2020; Shumake-Guillemot, J, et al, 2020; Bose-O’Reilly etal.,

2021). Complex humanitarian emergencies were aggravated, with vulnerable populations facing the combined risks of conﬂict,

displacement, COVID-19 and climate impacts (FSIN, 2020). Compounding events are not only found in low-income countries but also in

medium- and high-income countries, for instance in the case of COVID-19 and heatwaves (Shumake-Guillemot etal., 2020; Bose-O’Reilly

etal., 2021).

Responses and implications for adaptation and climate resilient development

The pandemic emphasises the inter-connected and compound nature of risks, vulnerabilities and responses to emergencies that

are simultaneously local and global (high conﬁdence). COVID-19 is often considered a more ‘explosive’ risk than the more gradual

anthropogenic climate change. However, many climate-related risks do already appear as severe shocks at smaller scales, and infrequent

or unprecedented extreme weather-related events often warrant similar rapid responses (Dodds etal., 2020; Gebreslassie, 2020; Hynes

etal., 2020; Phillips etal., 2020; Schipper, 2020; Semenza etal., 2021; illustrated in FigureCOVID.1). Individuals, households, sub-

national and national entities, and international organisations had generally delayed responses or denied the pandemic’s severity before

responding at the scale and urgency required, a pattern that resembles the international action required on climate change (Polyakova

etal., 2020; Shrestha etal., 2020).

Improved contingency and recovery planning, including disease mitigation measures, were crucial in responding to the pandemic in

similar ways to those seen in the aftermath of climate-related disasters (Guo etal., 2020; Ebrahim etal., 2020; Baidya etal., 2020;

Shultz etal., 2020; Mukherjee etal., 2020). The pandemic highlighted the lack of global and country-speciﬁc capacity to respond to an

unexpected and unplanned event and the need to implement more ﬂexible detection and response systems (Ebi etal., 2021b).

It also exposed underlying vulnerabilities, such as the lack of water access and healthcare in select low- and middle-income countries

and among indigenous and marginalised groups in high-income countries (Section4.4.3; Box4.3; 5.12.1). Increased risks of COVID-19

transmission emerged in crowded areas such as urban settings, refugee camps, detention centres and some workplaces, including in rural

settings (Brauer etal., 2020; Ramos etal., 2020; Staddon etal., 2020; Haddout etal., 2020). Public health responses to the COVID-19

pandemic, such as mandates for social distancing and advice for frequent handwashing, underlined the need for access to water and

sanitation facilities and wastewater management. However, they also sometimes interfered with access, for example, in evacuation and

shelter infrastructure during climate-related disasters (Armitage and Nellums, 2020; Adelodun etal., 2020; Poch etal., 2020; Hallema

etal., 2020; Patel etal., 2020; Espejo etal., 2020).

The experience of COVID-19 demonstrates that many warnings about the risks of the emergence of zoonotic transmission (‘delay is

costly’, ‘adapt early’ and ‘prevention pays’) did not result in sufﬁcient political attention, funding and pandemic prevention. In some

countries, there has been an increased awareness of the risks and the real or perceived trade-offs associated with risk management (e.g.,

economy compared with health and impacts compared with adaptation). Building trust and participatory processes and establishing

Cross-Chapter BoxCOVID (continued)

1069

Health, Wellbeing and the Changing Structure of Communities Chapter 7

stronger relationships with communities and other civic institutions may enable a recalibration of how the government responds to crises

and society–government relationships more generally (Amat etal., 2020; Deslatte, 2020).

The management of the COVID-19 pandemic has highlighted the value of scientiﬁc (including medical and epidemiological) expertise and

the importance of fast, accurate and comprehensive data to inform policy decisions and to anticipate and manage risk (high conﬁdence). It

emphasises the importance of effective communication of scientiﬁc knowledge (Semenza etal., 2021), decision-making under uncertainty

and decision frameworks that navigate different values and priorities. Successful policy responses were based on the emerging data,

medical advice and collaboration with a wider set of societal stakeholders beyond public health experts. For instance, experience in

Aotearoa, New Zealand, highlights the importance of pandemic responses attuned to the needs of different sociocultural groups and

Indigenous Peoples in particular. Their strengths-based COVID-19 response goes beyond identifying vulnerabilities to unlocking the

resources, capabilities and potential that might otherwise be latent in communities (McMeeking and Savage, 2020). As far as the value

of information for risk management is concerned, compared to the initial uncertainties regarding COVID-19, data about near- and

longer-term climate-related hazards is generally very good; however, high-quality and dense meteorological data are often still lacking in

lower income countries (Otto etal., 2020). Health data are particularly difﬁcult to obtain in real time, as is the case for biodiversity data,

which has a time lag of years before being made available and for which there is no coordinated monitoring, hampering effective risk

management (Navarro etal., 2017). Therefore, both epidemiological and meteorological forecasts would beneﬁt from more focus on (a)

decision support, (b) conveying uncertainty and (c) capturing vulnerability (Coughlan de Perez etal., 2021).

There is a considerable evidence base of speciﬁc actions that have co-beneﬁts for reducing pandemic and climate change risks while

enhancing social justice and biodiversity conservation (high conﬁdence). The pandemic highlighted aspects of risk management that have

long been recognised but are often not reﬂected in national and international climate policy: the value of addressing structural vulnerability

rather than taking speciﬁc measures to control single hazards and drivers of risk and the importance of decision-making capacities and

transparency, the rule of law, accountability and addressing inequities (or social exclusion) (reviewed by Pelling etal. (2021); see also

FigureCOVID.1).

Comprehensive and integrated risk management strategies can enable countries to address both the current pandemic and increase

resilience against climate change and other risks (Reckien, 2021; Semenza etal., 2021; Ebi etal., 2021b). In particular, given their immense

scale, COVID-19 recovery investments may offer an opportunity to contribute to climate resilient development pathways (CRDPs) through

a green, resilient, healthy and inclusive recovery (high conﬁdence) (Sovacool etal., 2020; Rosenbloom and Markard, 2020; Lambert etal.,

2020; Boyle etal., 2020; Bouman etal., 2020; UN DRR Asia-Paciﬁc, 2020; Brosemer etal., 2020; Dodds etal., 2020; Hynes etal., 2020;

Markard and Rosenbloom, 2020; Phillips etal., 2020; Schipper, 2020; Willi etal., 2020; Semenza etal., 2021; Pasini and Mazzocchi, 2020;

Meige etal., 2020; Pelling etal., 2021). However, windows of opportunity to enable such transitions are only open for a limited period

and need to be swiftly acted upon to effect change (high conﬁdence) (Chapter 18; Weible etal., 2020; Reckien, 2021). Initial indications

suggest that only USD1.8 trillion of the greater than USD17 trillion COVID-19-related stimulus ﬁnancing by G20countries and other major

economies that was committed up until mid-2021 contributed to climate action and biodiversity objectives, with signiﬁcant differences

between countries and sectors (Vivideconomics, 2021). Moreover, responses to previous crises (e.g., the 2008–2011 global ﬁnancial

crisis) demonstrate that despite high ambitions during the response phase, opportunities for reform do not necessarily materialise (Bol

etal., 2020; Boin etal., 2005). In addition, heightened societal and political attention to one crisis often comes at the cost of other policy

priorities (high conﬁdence) (Maor, 2018; Tosun etal., 2017), which could affect investments for climate resilient development (Hepburn

etal., 2020; WHO, 2020a; Bateman etal., 2020; Meige etal., 2020; Semenza etal., 2021).

In summary, the emerging literature suggests that the COVID-19 pandemic has aggravated climate-related health risks, demonstrated the

global and local vulnerability to cascading shocks and illustrated the importance of integrated solutions that tackle ecosystem degradation

and structural vulnerabilities in human societies. This highlights the potential and urgency of interventions that reduce pandemic and

climate change risks while enhancing compound resilience, social justice and biodiversity conservation (see FigureCOVID.1).

Cross-Chapter BoxCOVID (continued)

1070

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Hazard

Deforestation;

Society/nature alienation

Vulnerability

Fragile livelyhoods;

Fragile health

Exposure

Overcrowded housing;

Inadequate sanitation

Inadequate

response

Exclusive planning;

Uninformed decisions

Risk:

Compound risk to

pandemic and climate

change

Hazard reduction

Reforestation

Conservation of biodiversity

Vulnerability

reduction

Livelihood security;

Healthy lifestyles

Exposure reduction

Adaptive housing;

Adaptive sanitation;

Response

Inclusive planning;

Informal decisions

Resilience:

Compound resilience

to pandemic and

climate change

Compound risk and compound resilience to pandemic and climate change

FigureCOVID.1 | Compound risk and compound resilience to pandemic and climate change. Source: Pelling etal. (2021).

Cross-Chapter BoxCOVID (continued)

1071

Health, Wellbeing and the Changing Structure of Communities Chapter 7

7.2.2.5 Other Water Shortage and Drought-Associated Diseases

and Health Outcomes

Water shortage and drought are associated with skin diseases

(Schachtel etal., 2021; Lundgren, 2018; Andersen and Davis, 2017;

Kaffenberger et al., 2017; Andersen and Davis, 2017), trachoma

(Ramesh et al., 2016) and violence (Epstein et al., 2020a); more

research is warranted in these areas for future assessment.

7.2.3 Observed Impacts on Non-communicable Diseases

NCDs are those that are not directly transmitted from one person to

another person; they impose the largest disease burden globally. NCDs

constitute approximately 80% of the burden of disease in high-income

countries; the NCD burden is lower in low- and middle-income countries

but are expected to rise (Bollyky etal., 2017). NCDs constitute a large

group of diseases driven principally by environmental, lifestyle and

other factors; those identiﬁed as being climate sensitive include non-

infectious respiratory disease, cardiovascular disease (CVD), cancer

and endocrine diseases including diabetes. Additionally, there are

potential interactions between multiple climate-sensitive NCDs and

food security, nutrition and mental health. The literature on climate

change and NCDs continues to develop. More recently, scientists have

identiﬁed key gaps in the calculation of the global burden of disease

due to environmental health factors (Shaffer etal., 2019).

7.2.3.1 Cardiovascular Diseases

CVDs are a group of disorders of the heart and blood vessels that

include coronary heart disease, cerebrovascular disease, peripheral

arterial disease, rheumatic heart disease, congenital heart disease, deep

vein thrombosis and pulmonary embolism. CVDs are the leading cause

of death globally and over three quarters of the world’s CVD deaths

now occur in low- and middle-income countries (Roth etal., 2020).

Climate change affects the risk of CVD through high temperatures

and extreme heat (assessed in Section 7.2.4.1) and through other

mechanisms (medium conﬁdence), though the degree to which non-

temperature risks may increase remains unclear. For example, exposure

to air pollutants including PM, ozone (via its precursors), black carbon,

oxides of nitrogen, oxides of sulphur, hydrocarbons and metals can invoke

pro-inﬂammatory and prothrombotic states, endothelial dysfunction

and hypertensive responses (Giorgini etal., 2017; Stewart etal., 2017).

Winter peaks in CVD events, associated with greater concentrations of

air pollutants, have been reported in a range of countries and climates

(Claeys et al., 2017; Stewart et al., 2017); however, the association

between air pollution, weather and CVD events is complex and seems

to differ between cold and warm months, particularly for gaseous

pollutants such as ozone (Shi etal., 2020).

Climate change is projected to increase the number and severity of

wildﬁres (Liu etal., 2015b; Youssouf etal., 2014) and the evidence

for wildﬁre smoke-related CVD morbidity and mortality is suggestive

of increased CVD morbidity and mortality risk (Chen et al., 2021a)

including signiﬁcant increases in certain cardiovascular outcomes (e.g.,

cardiac arrests) (Dennekamp etal., 2015). CVD risks to highly exposed

populations, such as ﬁreﬁghters, are clearer (Navarro etal., 2019) and

could increase with additional exposure driven by climate change.

Other climate-related mechanisms that may increase CVD risk include

reductions in physical activity related to hot weather (Obradovich etal.,

2017), sleep disturbance (Obradovich et al., 2017) and dehydration

(Lim etal., 2015; Frumkin and Haines, 2019). There is little literature

on how changes in winter weather may affect these risks. Saline

intrusion of groundwater related to sea level rise (Taylor etal., 2012)

may increase the salt intake of affected populations, a risk factor for

hypertension that has been observed to increase blood pressure in

exposed populations (Talukder etal., 2017; Al Nahian etal., 2018).

7.2.3.2 Non-communicable Respiratory Diseases

Lung diseases, including asthma, COPD and lung cancer, comprise the

largest subsets of non-communicable pulmonary disease (Ferkol and

Schraufnagel, 2014). Overall, the global burden of non-communicable

lung disease including all chronic lung disease and lung cancer is

substantial, being responsible for 10.6% of deaths and 5.9% of DALYs

globally in 2019 (Vos etal., 2020).

Several non-communicable respiratory diseases are climate sensitive

based on their exposure pathways (very high conﬁdence). Multiple

exposure pathways contribute to non-communicable respiratory

disease (Deng etal., 2020), some of which are climate-related (Rice

etal., 2014), including mobilisation and transport of dust (Schweitzer

etal., 2018); changes in concentrations of air pollutants such as small

particulates (PM2.5) and ozone formed by photochemical reactions

sensitive to temperature (Hansel et al., 2016); increased wildland

ﬁres and related smoke exposure (Johnston etal., 2002; Reid etal.,

2016); increased exposure to ambient heat driving reduced lung

function and exacerbations of chronic lung disease (Collaco etal.,

2018; Jehn etal., 2013; McCormack etal., 2016; Witt etal., 2015) and

modiﬁcation of aeroallergen production and duration of exposure

(Ziska etal., 2019).

Burdens of allergic disease, particularly allergic rhinitis and allergic

asthma may be changing in response to climate change (medium

conﬁdence) (D’Amato etal., 2020; Eguiluz-Gracia etal., 2020; Deng

etal., 2020; Demain, 2018). This is supported by evidence showing an

increase in the length of the North American pollen season attributable

to climate change (Ziska etal., 2019), an association between timing

of spring onset and higher asthma hospitalisations presumed to be due

to higher pollen exposure (Sapkota etal., 2020) and other evidence

linking aeroallergen exposure with a worsening burden of allergic

disease (Demain, 2018; Poole etal., 2019).

7.2.3.3 Cancer

Climate change is likely to increase the risk of several malignancies

(high conﬁdence), though the degree to which risks may increase

remains unclear. Cancers, also known as malignant neoplasms,

include a heterogeneous collection of diseases with various causal

pathways, many with environmental inﬂuences. Malignant neoplasms

impose a substantial burden of disease globally and are responsible

for slightly more than 10 million deaths and 251 million DALYs

1072

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

globally in 2019 (Vos etal., 2020). Climatic hazards affect exposure

pathways for several different chemical hazards associated with

carcinogenesis (Portier et al., 2010). Most relevant literature has

focused on elaborating potential pathways and producing qualitative

or quantitative estimates of effect, though there is limited literature

on current and projected impacts.

The vast majority of elaborated pathways point to increased risk; for

example, there is concern that climate change may alter the fate and

transport of carcinogenic polyaromatic hydrocarbons (Domínguez-

Morueco etal., 2019) and increase mobilisation of carcinogens such

as bromide (Regli etal., 2015), persistent organic pollutants (POPs)

including polychlorinated-biphenyls that have accumulated in areas

contaminated by industrial runoff (Miner etal., 2018) and radioactive

material (Evangeliou etal., 2014). Exposure to these known carcinogens

can occur through multiple environmental media and can be increased

by climate change, for example through increased ﬂooding related

to extreme precipitation events and mobilisation of sediment where

carcinogens have accumulated (León etal., 2017; Santiago and Rivas,

2012). In addition, there is concern that changes in ultraviolet light

exposure related to shifts in precipitation may increase the incidence

of malignant melanoma, particularly for outdoor workers (Modenese

et al., 2018). Other harmful pathways include migration of and

increased exposure to liver ﬂukes, which cause hepatobiliary cancer

(Prueksapanich etal., 2018) and the introduction of infectious diseases

such as schistosomiasis that increase cancer risk due to climate-related

migration (Ahmed etal., 2014). Increased exposure to carcinogenic

toxins via multiple pathways is also a concern. Aﬂatoxin exposure, for

example, is expected to increase in Europe (Moretti etal., 2019), India

(Shekhar etal., 2018), Africa (Gnonlonﬁn etal., 2013; Bandyopadhyay

etal., 2016) and North America (Wu etal., 2011). Other carcinogenic

toxins originate from cyanobacteria blooms (Lee etal., 2017a), which

are projected to increase in frequency and distribution with climate

change (Wells etal., 2015; Paerl etal., 2016; Chapra etal., 2017).

7.2.3.4 Diabetes

Individuals suffering from diabetes are at higher risk of heat-related

illness and death (medium conﬁdence). Extreme weather events

and rising temperatures have been found to increase morbidity and

mortality in patients living with diabetes, especially in those with

cardiovascular complications (Méndez-Lázaro etal., 2018; Zilbermint,

2020; Hajat etal., 2017). Evidence suggests that the local heat loss

response of skin blood ﬂow is affected by diabetes-related impairments,

resulting in lower elevations in skin blood ﬂow in response to a heat

or pharmacological stimulus. Thermoregulatory sweating may also be

diminished by type-2 diabetes, impairing the body’s ability to transfer

heat from its core to the environment (Xu etal., 2019b). Higher rates

of doctor consultations by patients with type-2 diabetes and diabetics

with cardiovascular comorbidities have been observed during hot days,

but without evidence of heightened risk of renal failure or neuropathy

comorbidities (Xu etal., 2019b).

People with chronic illnesses are at particular risk during and after

extreme weather events due to treatment interruptions and lack of

access to medication (medium conﬁdence). The impacts of extreme

weather events on the health of chronically ill people are due to a

range of factors including disruption of transport, weakened health

systems including drug supply chains, loss of power and evacuations

of populations (Ryan et al., 2015a). Evacuations also pose speciﬁc

health risks to older adults (especially those who are frail, medically

incapacitated or residing in nursing or assisted living facilities) and

may be complicated by the need for concurrent transfer of medical

records, medications and medical equipment (Becquart etal., 2018;

Quast and Feng, 2019; US Global Change Research Program, 2016).

Emergency room visits after Hurricane Sandy rose among individuals

with type-2 diabetes (Velez-Valle etal., 2016).

7.2.4 Observed Impacts on Other Climate-Sensitive

Health Outcomes

7.2.4.1 Heat- and Cold-Related Mortality and Morbidity

Extreme heat events and extreme temperature have well-documented,

observed impacts on health, mortality (very high conﬁdence) and

morbidity (high conﬁdence). AR5 described the thermoregulatory

mechanisms and responses, including acclimatisation, linking heat,

cold and health, and these have been further conﬁrmed by recent

studies and reviews (e.g., Giorgini et al., 2017; Ikaheimo, 2018;

McGregor et al., 2015; Stewart et al., 2017; Schuster et al., 2017;

Zhang etal., 2018b). The health impacts of heat manifest clearly in

periods of extreme heat often codiﬁed as heatwaves. For example,

heatwaves across Europe (2003), Russia (2010), India (2015) and

Japan (2018) resulted in signiﬁcant death tolls and hospitalisations

(McGregor etal., 2017; Hayashida et al., 2019). Heat continues to

pose a signiﬁcant health risk due to increases in exposure, changes in

the size and spatial distribution of the human population, mounting

vulnerability and an increase in extreme heat events (high conﬁdence)

(Harrington etal., 2017; Liu etal., 2017; Mishra etal., 2017; Rohat

etal., 2019a; Rohat etal., 2019b; Rohat etal., 2019c; Watts etal.,

2019). Some regions are already experiencing heat stress conditions

approaching the upper limits of labour productivity and human

survivability (high conﬁdence). These include the Persian Gulf and

adjacent land areas, parts of the Indus River Valley, eastern coastal

India, Pakistan, northwestern India, the shores of the Red Sea, the

Gulf of California, the southern Gulf of Mexico and coastal Venezuela

and Guyana (Krakauer etal., 2020; Li etal., 2020; Raymond etal.,

2020; Saeed etal., 2021; Xu etal., 2020).

Under a variety of methods, estimates of the world’s population

exposed to extreme heat indicate very large and growing numbers

and an increase since pre-industrial times. For example, Li etal. (2020)

estimate that globally, 1.28 billion people each year experience

heatwave conditions similar to that of the lethal Chicago 1995 event,

compared with 0.99billion people that would be similarly exposed

under a pre-industrial climate. Further, for the 150 most populated

cities of the world, a 500% increase in the exposure to extreme heat

events occurred over the 1980–2017 period (Li etal., 2021), while for

the 1986–2005 period, the total exposure to dangerous heat in Africa’s

173 largest cities was 4.2billion person-days yr

–1

(Rohat etal., 2019a).

Globally the present exposure to heatwave events is estimated to be

14.8billion person-days yr

–1

, with the greatest cumulative exposures

measured in person-days occurring across southern Asia (7.19billion),

1073

Health, Wellbeing and the Changing Structure of Communities Chapter 7

sub-Saharan Africa (1.43billion), and north Africa and the Middle East

(1.33billion) (Jones etal., 2018).

The country level percentage of mortality attributable to non-

optimum temperature (heat and cold) has been found to range from

3.4 to 11% (Gasparrini etal., 2015; Zhang etal., 2019b). Heat as a

health risk factor has largely been overlooked in low- and middle-

income countries (Campbell etal., 2018; Green etal., 2019; Dimitrova

et al., 2021). For 2019, the GBD report estimates the burden of

DALYs attributable to low temperature was 2.2times greater than

the burden attributable to high temperature. However, this global

ﬁgure obscures important regional variations. Countries with a

high sociodemographic index—mainly mid-latitude high-income

temperate to cool climate countries— were found to have a cold-

related burden 15.4times greater than the heat-related burden, while

for warm lower-income regions, such as south Asia and sub-Saharan

Africa, the heat-related burden was estimated to be 1.7times and

3.6 times greater, respectively (Murray et al., 2020). For countries

where data availability permits, there is evidence that extreme

heat (and extreme cold) leads to higher rates of premature deaths

(Armstrong etal., 2017; Cheng etal., 2018; Costa etal., 2017).

Rapid changes and variability in temperatures are observed to increase

heat-related health and mortality risks, the outcomes varying across

temperate and tropical regions (Guo etal., 2016; Cheng etal., 2019; Kim

etal., 2019a; Tian etal., 2019; Zhang etal., 2018b; Zhao etal., 2019).

Several lines of evidence point to a possible decrease in population

sensitivity to heat, albeit mainly for high-income countries (high

conﬁdence), arising from the implementation of heat warning systems,

increased awareness and improved quality of life. (Sheridan and

Allen, 2018). Evidence suggests a general decrease in the impact of

heat on daily mortality (Diaz etal., 2018; Kinney, 2018; Miron etal.,

2015), a decline in the relative risk attributable to heat (Åström etal.,

2018; Barreca etal., 2016; Petkova etal., 2014) and an increase in the

minimum mortality temperature (MMT) (Åström etal., 2018; Folkerts

etal., 2020; Follos etal., 2021; Chung etal., 2018; Todd and Valleron,

2015; Yin etal., 2019). It is difﬁcult to draw conclusions regarding

trends in heat sensitivity for low- to middle-income countries and

speciﬁc vulnerable groups as these are under-represented in the

literature (Sheridan and Allen, 2018). Trends in heat sensitivity are

generally believed to be scale and situation dependent, but there is

considerable variability in changes in heat sensitivity as measured

by trends in heat-related mortality or MMT (Follos etal., 2021; Kim

etal., 2019a; Lee etal., 2021), with notable variability across different

population groups (Lu etal., 2021).

Temperature interacts with heat-sensitive physiological mechanisms

via multiple pathways to affect health. In the worst cases, these lead

to organ failure and death (Mora et al., 2017a; Mora et al., 2017b).

Excess deaths during extreme heat events occur predominantly in older

individuals and are overwhelmingly cardiovascular in origin (very high

conﬁdence). A higher occurrence of CVD mortality in association with

prolonged period of low temperatures has been well documented

globally (Giorgini etal., 2017; Stewart etal., 2017); however, there is

growing evidence that cardiovascular deaths are more related to heat

events than cold spells (Chen etal., 2019; Liu etal., 2015a; Bunker etal.,

2016). While there is strong association between ambient temperature

and cardiovascular events globally, there are complex interactions and

modulators of individual response (Wang etal., 2017b). Further, some

CVD morbidity sub-groups such as myocardial infarction (MI) and stroke

hospitalisation display temperature sensitivity while others do not

(Bao etal., 2019; Sun etal., 2018; Wang etal., 2016). Although older

adults have inherent sensitivities to temperature-related health impacts

(Bunker etal., 2016; Phung etal., 2016), children can also be affected by

extreme heat (Xu etal., 2014). Cardiovascular capacity or health is also a

critical determinant of individual health outcomes (Schuster etal., 2017).

Medications to treat CVDs, such as diuretics and beta-blockers, may

impair resilience to heat stress (Stewart etal., 2017). Other mediating

factors in the causal pathway range from alcohol consumption (Cusack

etal., 2011; Epstein and Yanovich, 2019) and obesity (Speakman, 2018)

to pre-existing conditions, such as diabetes and hyperlipidaemia, and

urban characteristics (Chen etal., 2019; Sera etal., 2019).

Under extreme heat conditions, increases in hospitalisations have

been observed for ﬂuid disorders, renal failure, urinary tract infections,

septicaemia, general heat stroke as well as unintentional injuries (Borg

etal., 2017; Phung etal., 2017; Goggins and Chan, 2017; Hayashida

et al., 2019; Hopp et al., 2018; Ito et al., 2018; Kampe et al., 2016;

McTavish etal., 2018; Ponjoan etal., 2017; van Loenhout etal., 2018).

Hospitalisations and mortality due to respiratory disorders also occur

during heat events with the interactive role of air quality being important

for some locations but not others (Krug etal., 2019; Pascal etal., 2021;

Patel etal., 2019). Increased levels of heat-related hospitalisation also

manifest in elevated levels of emergency service calls (Cheng etal., 2016;

Guo, 2017; Papadakis etal., 2018; Williams etal., 2020).

Heat- and cold-related health outcomes vary by location (Dialesandro

etal., 2021; Hu etal., 2019; Phung etal., 2016), suggesting outcomes

are highly moderated by socioeconomic, occupational and other

non-climatic determinants of individual health and socioeconomic

vulnerability (Åström et al., 2020; McGregor et al., 2017; McGregor

etal., 2017; Schuster etal., 2017; Benmarhnia etal., 2015; Watts etal.,

2019) (high conﬁdence). For example, access to air conditioning is an

important determinant of heat-related health outcomes for some

locations (Guirguis etal., 2018; Ostro etal., 2010). Although there is a

paucity of global level studies of the effectiveness of air conditioning for

reducing heat-related mortality, a recent assessment indicates increases

in air conditioning explains only part of the observed reduction in heat-

related excess deaths, amounting to 16.7% in Canada, 20.0% in Japan,

14.3% in Spain and 16.7% in the US (Sera etal., 2020).

Signiﬁcant effects of heat exposure are evident in sport and work

settings with exertional heat illness leading to death and injury

(Adams and Jardine, 2020). Although most studies of heat-related

sports injuries refer to high-income countries, these point to an

increasing number of heat injuries with widening participation in

sport and an increasing frequency of extreme heat events. The highest

rates of exertional heat illness are reported for endurance type events

(running, cycling and adventure races), American football and athletics

(Gamage et al., 2020; Grundstein et al., 2017; Kerr et al., 2020;

McMahon etal., 2021; Yeargin etal., 2019). The health, safety and

productivity consequences of working in extreme heat are widespread

(Ma etal., 2019; Morabito etal., 2021; Kjellstrom etal., 2019; Orlov

1074

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

etal., 2020; Smith etal., 2021; Vanos etal., 2019; Varghese etal., 2020;

Williams etal., 2020). Occupational heat strain in outdoor workers

manifests as dehydration, mild reduction in kidney function, fatigue,

dizziness, confusion, reduced brain function, loss of concentration and

discomfort (Al-Bouwarthan etal., 2020; Boonruksa etal., 2020; Habibi

etal., 2021; Levi etal., 2018; Venugopal etal., 2021; Xiang etal., 2014).

In the case of armed forces, a global review of the available literature

points to a slightly higher incidence of heat stroke in men compared

to women but a higher proportion of heat intolerance and greater

risk of exertional heat illness amongst women (Alele etal., 2020).

There is also some evidence that for healthcare workers, the risk of

occupational heat stress grew during the COVID-19 pandemic due to

the need to wear personal protective equipment (Foster etal., 2020;

Lee etal., 2020; Messeri etal., 2021). Based on a systematic review of

the literature, one study estimates global costs from heat-related lost

work time were USD 280billion in 1995 and USD 311billion in 2010,

with low- and middle-income countries and countries with warmer

climates experiencing greater losses as a proportion of gross domestic

project (GDP) (Borg etal., 2021). Other global level assessments note

an increase in the potential hours of work lost due to heat over

the 2000–2018 period; in 2018, 133.6billion potential work hours

were lost, amounting to 45billion hours more than in 2000 (Watts

etal., 2019). For China, heat-related productivity losses have been

estimated at 9.9billion hours in 2019, equivalent to 0.5% of the total

national work hours for that year, with Guangdong province, one of

the warmest regions in China, accounting for almost a quarter of the

losses (Cai etal., 2021).

Wide ranging knowledge regarding the speciﬁc detection of heat- and

cold-related mortality/morbidity and its attribution to observed climate

change is lacking. Although there has been an observed increase in

winter-season temperatures for a number of regions, to date there is

variable evidence for a consequential reduction in winter mortality and

susceptibility to cold over time due to milder winters; some countries

demonstrate decreasing trends, while other countries show stable or

even increasing trends in cold-attributable mortality fractions over time

(e.g., Arbuthnott etal. (2020); Åström etal. (2013); Diaz etal. (2019);

Hajat (2017); Hanigan etal. (2021); Lee etal. (2018b)). While there

is a burgeoning literature on the attribution of extreme heat events

to climate change (e.g., Vautard etal. (2020)), the number of studies

that assess the extent to which observed changes in heat-related

mortality may be attributable to climate change is small (Ebi etal.,

2020). During the 2003 European heatwave, anthropogenic climate

change increased the risk of heat-related mortality by approximately

70% and 20% for London and Paris, respectively (Mitchell etal., 2016).

For the severe heat event across Egypt in 2015, the impact on human

discomfort was 69% (±17%) more likely due to anthropogenic climate

change (Mitchell, 2016), and for Stockholm, Sweden, it has been

estimated that mortality due to temperature extremes for 1980 to

2009 was double what would have occurred without climate change

(Åström etal., 2013). To date there has only been one multi-country

attempt to quantify the heat-related human health impacts that have

already occurred due to climate change. Based on an analysis of 732

locations spanning 43countries for the 1991–2018 period, the study

found that on average 37.0% (inter-quartile range 20.5–76.3%) of

warm-season heat-related deaths can be attributed to anthropogenic

climate change, equivalent to an average mortality rate of 2.2/100,000

(median: 1.67/100,000; interquartile range: 1.08–2.34/100,000).

Regions with a high attributed percentage (>50%) include southern

and western Asia (Iran and Kuwait), Southeast Asia (Philippines and

Thailand) and several countries in Central and South America. Those

with lower values (<35%) include Western Europe (the Netherlands,

Germany and Switzerland), eastern Europe (Moldova, the Czech

Republic and Romania), southern Europe (Greece, Italy, Portugal

and Spain), North America (USA) and eastern Asia (China, Japan and

South Korea) (Vicedo-Cabrera etal., 2021). Due to data restrictions,

some of the poorest and most susceptible regions to climate change

and increases in heat exposure, such as west and east Africa (Aseﬁ-

Najafabady etal., 2018; Sylla etal., 2018) and south Asia, could not be

included in the analysis (Mitchell, 2021).

7.2.4.2 Injuries Arising from Extreme Weather Events Other

than Heat and Cold

Injuries comprise a substantial portion of the global burden of disease.

In 2019, injuries comprised 9.82% of total global DALYs and 7.61%

of deaths (Vos etal., 2020). The causal pathways for many injuries,

particularly those from heat and extreme weather events, ﬂooding

and ﬁres, exhibit clear climate sensitivity (Roberts and Arnold,

2007; Roberts and Hillman, 2005), as do some injuries occurring in

occupational settings (Marinaccio et al., 2019; Sheng et al., 2018),

but a comprehensive assessment of climate sensitivity in injury causal

pathways has not been done. Certain groups, including Indigenous

Peoples, children and the elderly (Ahmed etal., 2020) are at greater

risk for a wide range of injuries. Extreme events impose substantial

disease burden directly as a result of traumatic injuries, drowning and

burns and large mental health burdens associated with displacement

(Fullilove, 1996), depression and post-traumatic stress disorder (PTSD),

but the overall injury burden associated with extreme weather is not

known. It is known that the Asia-Paciﬁc region has experienced the

highest relative burden of injuries from extreme weather in recent

decades (Hashim and Hashim, 2016).

Extreme weather imposes a substantial morbidity and mortality burden

that is quite variable by location and hazard. The proportion of this

burden related speciﬁcally to injuries is not established. From 1998 to

2017 there were 526,000 deaths from 11,500 extreme weather events,

and the average annual attributable all-cause mortality incidence

in the ten most affected countries was 3.5 per 100,000 population

(Eckstein etal., 2017). Rates can be much higher; mortality incidence

in Puerto Rico and Dominica from extreme weather were 90.2 and

43.7 per 100,000 population in 2017, respectively (Eckstein et al.,

2017). Not all of these deaths are from injuries, and the proportion

of mortality and morbidity associated with injuries varies by location

and hazard. One review found that one-year post-event prevalence

rates for injuries associated with extreme events (ﬂoods, droughts,

heatwaves and storms) in developing countries ranged from 1.4% to

37.9% (Rataj etal., 2016). Other literature has documented an increase

in the risk of motor vehicle accidents in association with extreme

precipitation (Liu etal., 2017; Stevens etal., 2019), temperature (Leard

and Roth, 2019) and sandstorms (Islam etal., 2019) and an increased

risk of traumatic occupational injuries associated with temperature

extremes, particularly extreme heat, likely from fatigue and decreased

psychomotor performance (Varghese etal., 2019).

1075

Health, Wellbeing and the Changing Structure of Communities Chapter 7

There is clear evidence of climate sensitivity for multiple injuries from

ﬂoods, ﬁres and storms, but there is a need for additional evidence

regarding the current injury burden attributable to climate change. It is

as likely as not that climate change has increased the current burden

of disease from injuries related to extreme weather, particularly in low-

income settings (low conﬁdence). Approximately 120million people

are exposed to coastal ﬂooding annually (Nicholls etal., 2007), causing

an estimated 12,000 deaths (Shultz etal., 2005), and there is signiﬁcant

concern for worsening ﬂooding associated with climate change (Shultz

etal., 2018a; Shultz etal., 2018b; Woodward and Samet, 2018) but

very limited quantiﬁcation of attributable burden. A range of adverse

health outcomes has been identiﬁed in a study of ﬁres in sub-zero

temperatures that are thought to be increasing in frequency due to

climate change (Metallinou and Log, 2017).

7.2.4.3 Observed Impacts on Maternal, Foetal and Neonatal

Health

Maternal and neonatal disorders accounted for 3.7% of total global

deaths and 7.8% of global DALYs in 2019 (Vos etal., 2020). Children

and pregnant women have potentially higher rates of vulnerability

and/or exposure to climatic hazards, extreme weather events and

undernutrition (Garcia and Sheehan, 2016; Sorensen et al., 2018;

Chersich et al., 2018). Available evidence suggests that heat is

associated with higher rates of pre-term birth (Wang et al., 2020),

low birthweight, stillbirth, neonatal stress (Cil and Cameron, 2017;

Kuehn and McCormick, 2017) and adverse child health (Kuehn and

McCormick, 2017). Extreme weather events are associated with

reduced access to prenatal care, unattended deliveries (Abdullah

etal., 2019) and decreased paediatric healthcare access (Haque etal.,

2019).

7.2.4.4 Observed Impacts on Malnutrition

Climate variability and change contribute to food insecurity that can

lead to malnutrition, including undernutrition, overweight and obesity,

and to disease susceptibility, particularly in low- and middle-income

countries (high conﬁdence). Since AR5, analyses of the links between

climate change and food expanded beyond undernutrition to include

the impacts of climate change on a wider set of diet- and weight-

related risk factors and their impacts on NCDs, along with the role

of dietary choices for GHG emissions (IPCC, 2019b) including dietary

inadequacy (deﬁciencies, excesses or imbalances in energy, protein

and micronutrients), infections and sociocultural factors (Global

Nutrition Report 2020). Undernutrition exists when a combination of

insufﬁcient food intake, health, and care conditions results in one or

more of the following: underweight for age, short for age (stunted),

thin for height (wasted), or functionally deﬁcient in vitamins and/

or minerals (micronutrient malnutrition or ‘hidden hunger’). Food

insecurity and poor access to nutrient-dense food contribute not only

to undernutrition but also to obesity and susceptibility to NCDs in

low- and middle-income countries (FAO etal., 2018; Swinburn etal.,

2019).

Globally, more than 690million people are undernourished, 144million

children are stunted (chronic undernutrition), 47million children are

wasted (acute undernutrition), and more than 2billion people have

micronutrient deﬁciencies (FAO, 2020). More than 135million people

across 55countries experienced acute hunger requiring urgent food,

nutrition and livelihood assistance in 2019 (FSIN/GNAFC, 2020).

The COVID-19 pandemic is projected to increase the number of

acutely food insecure people to 270million people (FSIN, 2020) and

worsen malnutrition levels (FAO etal., 2020; Rippin etal., 2020). The

relationships between climate change and obesity vary based on

geography, population sub-groups and/or stages of economic growth

and population growth (An etal., 2017). Increasing temperatures could

contribute to obesity through reduced physical activity, increased

prices of produce or shifts in eating patterns of populations towards

more processed foods (An etal., 2018). In the largest global study

to date exploring the connections between child diet diversity and

recent climate, data from 19countries in six regions (Asia, Central

America, South America, north Africa, southeast Africa and west Africa)

indicated signiﬁcant reductions in diet diversity associated with higher

temperatures and signiﬁcant increases in diet diversity associated with

higher precipitation (Niles etal., 2021).

Climate change can affect the four aspects of food security: food

production and availability, stability of food supplies, access to food

and food utilisation (IPCC, 2019b). Access to sufﬁcient food does not

guarantee nutrition security. Extreme weather and climate events

can result in inadequate or insufﬁcient food consumption, increasing

susceptibility to infectious diseases (Rodriguez-Llanes etal., 2016; Gari

etal., 2017; Kumar etal., 2016; Lazzaroni and Wagner, 2016) but also

to being overweight or obese and increasing susceptibility to non-

communicable diseases in low- and middle-income countries (FAO,

2018; Swinburn etal., 2019).

Nearly half of all deaths in children under ﬁve years of age are

attributable to undernutrition, putting children at greater risk of dying

from common infections. Undernutrition in the ﬁrst 1,000days of a

child’s life can lead to stunted growth, which can result in impaired

cognitive ability and reduced future school and work performance

and the associated costs of stunting in terms of lost economic growth

can be of the order of 10% of GDP yr

–1

in Africa (UNICEF/WHO/WBG,

2019).

At the same time, diseases associated with high-calorie, unhealthy diets

are increasing globally, with 38.3million overweight children under

ﬁve years of age (Global Nutrition Report, 2018), 2.1billion overweight

or obese adults and the global prevalence of diabetes almost doubling

in the past 30years (Swinburn etal., 2019). Unbalanced diets, such

as diets low in fruits and vegetables and high in red and processed

meat, are the number one risk factor for mortality globally and in most

regions (Gakidou etal., 2018; Afshin etal., 2019).

Socioeconomic factors that mediate the inﬂuence of climate change on

nutrition include cultural and societal norms; governance, institutions,

policies and fragility; human capital and potential; and social position

and access to healthcare, education and food aid (Rozenberg, 2017;

Alkerwi etal. 2015; Tirado, 2017; FAO etal., 2018; Global Nutrition

Report 2020). Extreme events may affect access to adequate diets,

leading to malnutrition and increasing the risk of disease (Beveridge

etal., 2019; Rodriguez-Llanes etal., 2016; Gari etal., 2017; Kumar

etal., 2016; Lazzaroni and Wagner, 2016; Thiede and Gray, 2020).

1076

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

7.2.4.5 Observed Impacts on Exposure to Chemical

Contaminants

Climate change in northern regions, including Arctic ecosystems,

is causing permafrost to thaw, creating the potential for mercury

(Hg) to enter the food chain (medium agreement, low evidence) as

methyl mercury (MeHg), which is highly neurotoxic and nephrotoxic

and bioaccumulates and biomagniﬁes throughout the food chain via

dietary uptake of ﬁsh, seafood and mammals. Mercury methylation

processes in aquatic environments have been found to be exacerbated

by ocean warming, coupled with more acidic and anoxic sediments

(FAO, 2020). Consumption of mercury-contaminated ﬁsh has been

found to be linked to neurological disorders due to methyl mercury

poisoning (i.e., Minamata disease) that is associated with climate

change-contaminant interactions that alter the bioaccumulation and

biomagniﬁcation of toxic and fat-soluble persistent organic pollutants

and polychlorinated biphenyls (PCBs) (Alava etal., 2017) in seafood

and marine mammals (medium conﬁdence). Indigenous Peoples have

a higher exposure to such risks because of the accumulation of such

toxins in traditional foods (J.J. etal., 2017). Contamination of food with

PCBs and dioxins has a range of adverse health impacts (Lake etal.,

2015).

Chapter 5 (Sections 5.4.3, 5.5.2.3, 5.8.1, 5.8.2, 5.8.3, 5.9.1, 5.11.1,

5.11.3, 5.12.3) discusses the possible impacts of climate change on

food safety, including exposure to toxigenic fungi, PCBs and other

POPs, mercury and harmful algal blooms.

Climate change may affect animal health management practices,

potentially leading to an increased use of pesticides or veterinary drugs

(such as preventive antimicrobials) that could result in increased levels

of residues in foods (high agreement, medium/low evidence) (Beyene

et al., 2015; FAO and WHO, 2018; European Food Safety Authority,

2020; MacFadden etal., 2018).

7.2.5 Observed Impacts on Mental Health and Well-Being

7.2.5.1 Observed Impacts on Mental Disorders

A wide range of climatic events and conditions have observed and

detrimental impacts on mental health (very high conﬁdence). The

pathways through which climatic events affect mental health are

varied, complex and inter-connected with other non-climatic inﬂuences

that create vulnerability (Figure 7.6). The climatic exposure may be

direct, such as experiencing an extreme weather event or prolonged

high temperatures, or indirect, such as mental health consequences

of undernutrition or displacement. Exposure may also be vicarious,

with people experiencing decreased mental health associated with

observing the impact of climate change on others or simply by learning

about climate change. Non-climatic moderating inﬂuences range

from an individual’s personality and pre-existing conditions, to social

support, and to structural inequities (Gariepy et al., 2016; Hrabok

etal., 2020; Nagy etal., 2018; Silva etal., 2016b). Depending on these

background and contextual factors, similar climatic events may result

in a range of potential mental health outcomes, including anxiety,

depression, acute traumatic stress, PTSD, suicide, substance abuse and

sleep problems, with conditions ranging from being mild in nature to

those that require hospitalisation (Berry etal., 2010; Cianconi etal.,

2020; Clayton etal., 2017; Ruszkiewicz etal., 2019; Bromet etal., 2017;

Lowe, 2019). The line between mental health and more general well-

being is permeable, but in this section we refer to diagnosable mental

disorders—conditions that disrupt or impair normal functioning

through impacts on mood, thinking or behaviour.

There is an observable association between high temperatures and

mental health decrements (high conﬁdence), with an additional

possible inﬂuence of increased precipitation (medium agreement,

medium evidence). Heat-associated mental health outcomes include

suicide (Williams etal., 2015a; Carleton, 2017; Burke etal., 2018; Kim

etal., 2019b; Thompson etal., 2018; Schneider etal., 2020; Cheng

etal., 2021; Baylis etal., 2018; Obradovich etal., 2018); psychiatric

hospital admissions and emergency room visits for mental disorders

(Hansen etal., 2008; Wang etal., 2014; Chan etal., 2018; Mullins and

White, 2019; Yoo etal., 2021); experiences of anxiety, depression and

acute stress (Obradovich etal., 2018; Mullins and White, 2019); and

self-reported mental health (Li etal., 2020). In Canada, Wang etal.

(2014) found an association between mean heat exposure of 28°C and

greater hospital admissions within 0 to 4days for mood and behavioural

disorders (including schizophrenia, mood and neurotic disorders). A US

study found mental health problems increased by 0.5% when average

temperatures exceed 30°C, compared to averages between 25°C

and 30°C; a 1°C warming over ﬁve years was associated with a 2%

increase in mental health problems (Obradovich etal., 2018). Another

study found a 1°C rise in monthly average temperatures over several

decades was associated with a 2.1% rise in suicide rates in Mexico and

a 0.7% rise in suicide rates in the USA (Burke etal., 2018). A systematic

review of published research using a variety of methodologies from

19countries (Thompson etal., 2018) found an increased risk of suicide

associated with a 1°C rise in ambient temperature.

Discrete climate hazards including storms (Kessler etal., 2008; Boscarino

et al., 2013; Boscarino et al., 2017; Obradovich et al., 2018), ﬂoods

(Baryshnikova and Pham, 2019), heatwaves, wildﬁres and drought

(Hanigan etal., 2012; Carleton, 2017; Zhong etal., 2018; Charlson etal.,

2021) have signiﬁcant negative consequences for mental health (very

high conﬁdence). A large body of research identiﬁes the impacts of

extreme weather events on PTSD, anxiety and depression; much of the

research has been done in the USA and the UK, but a growing number

of studies ﬁnd evidence for similar impacts on mental health in other

countries, including Spain (Foudi etal., 2017), Brazil (Alpino etal., 2016),

Chile (Navarro etal., 2016), Small Island Developing States (Kelman

etal., 2021) and Vietnam (Pollack etal., 2016). Approximately 20–30%

of those who live through a hurricane develop depression and/or PTSD

within the ﬁrst few months following the event (Obradovich etal., 2018;

Schwartz etal., 2015; Whaley, 2009), with similar rates for people who

have experienced ﬂooding (Waite etal., 2017; Fernandez etal., 2015).

Studies conducted in South America and Asia indicate an increase in

PTSD and depressive disorders after extreme weather events (Rataj

etal., 2016). Evidence is lacking for African countries (Otto etal., 2017).

Children and adolescents are particularly vulnerable to post-traumatic

stress after extreme weather events (Brown etal., 2017; Hellden etal.,

2021; Kousky, 2016), and increased susceptibility to mental health

problems may linger into adulthood (Maclean etal., 2016).

1077

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Climate change impacts on mental health and adaptation responses

Hazard

Acute events

(e.g. storms, floods, wildfires,

extreme heat)

Chronic changes

(e.g. drought, sea level rise, sea ice

loss, changing climate normals)

Vulnerability

Pre-existing health conditions

Socio-economic inequities

Gender

Age

Occupation

Exposure

Direct exposure(s)

Indirect exposure(s)

(e.g. displacement, food systems

disruption, occupational loss)

Vicarious exposure(s)

(e.g. observed experiences of others,

media depictions of climate change)

Key adaptation responses

Scale of adaptation

Institutional

State and non-state actors:

effective mental health systems, planning and

preparedness, informed policies, early

intervention

Local governments:

planning, design, green infrastructure

Community

- Supportive social networks, effective

information channels

Individuals

- awareness, preparedness, mental health

support, nature-based therapy

Risks to mental health and wellbeing

2. Vulnerability

Physiological factors

Social factors

3.Exposure

Direct exposure(s)

Indirect exposure(s)

Vicarious exposure(s)

4. Response

Institutional

Community

Individuals

1. Hazard

Acute events

Chronic changes

5. Risks

to mental health

and wellbeing

Mental illness

[e.g., PTSD, depression, suicide]

Diminished wellbeing

[e.g., stress, climate anxiety, cognitive impairment )

Diminished social relations

[e.g., loss of culture, interpersonal violence)

Figure7.6 | Climate change impacts on mental health and key adaptation responses.

PTSD: Post traumatic stress disorder.

1078

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Wildﬁres have observed negative impacts on mental health (high

conﬁdence). This is due to the trauma of the immediate experience

and/or subsequent displacement and evacuation (Dodd etal., 2018;

Brown etal., 2019; Psarros etal., 2017; Silveira etal., 2021b). Sub-

clinical outcomes, such as increases in anxiety, sleeplessness or

substance abuse are reported in response to wildﬁres and extreme

weather events, with impacts being pronounced among those who

experience greater losses or are more directly exposed to the event;

this may include ﬁrst responders.

Mental health impacts can emerge as result of climate impacts on

economic, social and food systems (high conﬁdence). For example,

malnutrition among children has been associated with a variety of

mental health problems (Adhvaryu et al., 2019; Hock et al., 2018;

Yan etal., 2018), as has food insecurity among adults (Lund et al.,

2018). The economic impacts of droughts have been associated with

increases in suicide, particularly among farmers (Carleton, 2017;

Edwards etal., 2015; Vins etal., 2015); those whose occupations are

likely to be affected by climate change report that it is a source of

stress that is linked to substance abuse and suicidal ideation (Kabir,

2018). Studies of Indigenous Peoples often describe food insecurity or

reduced access to traditional foods as a link between climate change

and reduced mental health (Middleton etal., 2020b). The loss of family

members, for example due to an extreme weather event, increases

the risk of mental illness (Keyes etal., 2014). Individuals in low- and

middle-income countries may be more severely impacted due to lesser

access to mental health services and lower ﬁnancial resources to help

cope with impacts compared with high-income countries (Abramson

etal., 2015).

Anxiety about the potential risks of climate change and awareness

of climate change itself can affect mental health even in the absence

of direct impacts (low conﬁdence). There is a need for more evidence

about the prevalence or severity of climate change-related anxiety,

sometimes called ecoanxiety, but national surveys in the USA, Europe

and Australia show that people express high levels of concern and

perceived harm associated with climate change (Steentjes et al.,

2017; Clayton and Karazsia, 2020; Cunsolo and Ellis, 2018; Helm

etal., 2018; Leiserowitz etal., 2017; Reser etal., 2012; Steentjes etal.,

2017). In a US sample, perceived ecological stress, deﬁned as personal

stress associated with environmental problems, predicted depressive

symptoms (Helm etal., 2018); in a sample of Filipinos, climate anxiety

was correlated with lower mental health (Reyes etal., 2021) and a

non-random study in 25 countries showed positive correlations

between negative emotions about climate change and self-rated

mental health (Ogunbode etal., 2021). However, an earlier study found

no correlation between climate change worry and mental health issues

(Berry and Peel, 2015). Because the perceived threat of climate change

is based on subjective perceptions of risk and coping ability as well

as on experiences and knowledge (Bradley etal., 2014), even people

who have not been directly affected may be stressed by a perception

of looming danger (Clayton and Karazsia, 2020). Not surprisingly,

those who have directly experienced some of the effects of climate

change may be more likely to show such responses. Indigenous

Peoples, whose culture and well-being tend to be strongly linked to

local environments, may experience mental health effects associated

with changes in environmental risks; studies suggest connections to an

increase in depression, substance abuse or suicide in some Indigenous

Peoples (Canu etal., 2017; Cunsolo Willox etal., 2013; Middleton etal.,

2020b; Jaakkola etal., 2018).

7.2.5.2 Observed Impacts on Well-Being

Overall, research suggests that climate change has already had

negative effects on subjective well-being (medium conﬁdence).

Climate change can affect well-being through a number of pathways,

including loss of access to green and blue spaces due to damage

from storms, coastal erosion, drought or wildﬁres; heat; decreased air

quality; and disruptions to one’s normal pattern of behaviour, residence,

occupation or social interactions (Hayward and Ayeb-Karlsson, 2021).

For example, substantial evidence shows a negative correlation

between air pollution and subjective well-being or happiness (Apergis,

2018; Cunado and de Gracia, 2013; Lu, 2020; Luechinger, 2010;

Menz and Welsch, 2010; Orru etal., 2016; Yuan etal., 2018; Zhang

et al., 2017a); in the reverse direction, there is evidence not only

that time in nature but more speciﬁcally a feeling of connectedness

to nature are both associated with well-being (Martin etal., 2020)

and healthy ecosystems offer opportunities for health improvements

(Pretty and Barton, 2020). Negative emotions such as grief—often

termed ‘solastalgia’ (Albrecht etal., 2007)—are associated with the

degradation of local or valued landscapes (Eisenman etal., 2015; Ellis

and Albrecht, 2017; Polain etal., 2011; Tschakert etal., 2017; Tschakert

et al., 2019), which may threaten cultural rituals, especially among

Indigenous Peoples (Cunsolo and Ellis, 2018; Cunsolo et al., 2020).

Studies conducted in the Solomon Islands and Tuvalu found qualitative

and quantitative evidence of experiences of climate change and worry

about the future, with negative impacts on respondents’ well-being

(Asugeni etal., 2015; Gibson etal., 2020).

Heat is one of the best-studied aspects of climate change observed

to reduce well-being (high conﬁdence). Higher summer temperatures

are associated with decreased happiness and ratings of well-being

(Carleton and Hsiang, 2016; Miles-Novelo and Anderson, 2019;

Connolly, 2013; Noelke etal., 2016; Baylis etal., 2018; Moore etal.,

2019; Wang etal., 2020b). A study of 1.9million Americans (Noelke

etal., 2016) found that exposure to one day averaging 21°C–27°C was

associated with reduced well-being by 1.6% of a standard deviation

and days above 32°C were associated with reduced well-being

by 4.4% of a standard deviation relative to a reference interval of

10°C–16°C. A similar relationship between heat and mood has been

observed in China, where expressed mood began to decrease when

the average daily temperature was over 20°C (Wang etal., 2020b). The

causal mechanism is unclear but could be due to impacts on health,

economic costs or social interactions (Belkin and Kouchaki, 2017;

Osberghaus and Kühling, 2016) or reduced quality or quantity of sleep

(Fujii etal., 2015; Obradovich etal., 2017; Obradovich and Migliorini,

2018). Heat has also been associated with inter-personal and inter-

group aggression and increases in violent crime (Heilmann etal., 2021;

Mapou etal., 2017; Tiihonen etal., 2017). For the most part, studies

have measured daily response to average daily temperatures and are

unable to predict whether the effect is cumulative in response to a

sequence of unusually warm days. However, there is no evidence that

adaptation occurs over time to eliminate the negative response to very

warm temperatures (Moore etal., 2019). Some research has found

1079

Health, Wellbeing and the Changing Structure of Communities Chapter 7

a negative effect of extreme cold on well-being (Yoo etal., 2021);

increasing winter temperatures associated with climate change could

serve to compensate for the impact of increased summer temperatures.

However, the effect of high temperatures is typically found to be

stronger than the effect of low temperatures, and in some cases no

detrimental impacts of cold weather are found (Almendra etal., 2019;

Mullins and White, 2019).

Climate change also threatens well-being deﬁned in terms of

capabilities or the capacity to fulﬁl one’s potential and fully participate

in society. Heat can limit labour capacity; one study estimated that

45billion hours of labour productivity were lost in 2018 compared

to 2000 due to high temperatures (Watts et al., 2019). Both heat

and air pollution also impair human capabilities through a negative

effect on cognitive performance (Taylor etal., 2016b) and even impair

skills acquisition, reducing the ability to learn (Park et al., 2021)

and affecting marginalised groups more strongly (Park etal., 2020),

although ﬁndings are inconsistent and depend in part on the nature of

the task (low conﬁdence).

Systematic reviews have found an association between higher

ambient levels of ﬁne airborne particles with cognitive impairment

in the elderly and with behavioural problems (related to impulsivity

and attention problems) in children (Power etal., 2016; Yorifuji etal.,

2017; Younan etal., 2018; Zhao etal., 2018b) (medium conﬁdence).

Malnutrition has also been associated with reduced educational

achievement and long-term decrements in cognitive function

(Acharya etal., 2019; Asmare etal., 2018; Na etal., 2020; Kim etal.,

2017; Talhaoui etal., 2019).

7.2.6 Observed Impacts on Migration

Consistent with peer-reviewed scholarship and with the United

Nations Framework Convention on Climate Change (UNFCCC) Cancun

Adaptation Framework section 14(f) and the Paris Agreement, this

Chapter assesses the impacts of climate change on four types of

migration: (a) adaptive migration (i.e., where migration is an outcome

of individual or household choice), (b) involuntary migration and

displacement (i.e., where people have few or no options except to

move), (c) organised relocation of populations from sites highly exposed

to climatic hazards and (d) immobility (i.e., an inability or unwillingness

to move from areas of high exposure for cultural, economic or social

reasons) (Cross-Chapter BoxMIGRATE in Chapter 7).

A general theme across studies from all regions is that climate-related

migration outcomes are diverse (high conﬁdence) and may be manifest

as decreases or increases in migration ﬂows, and may lead to changes

in the timing or duration of migration and to changes in migration

source locations and destinations. Multi-country studies of climatic

impacts on migration patterns in Africa have found that migration

exhibits weak, inconsistent associations with variations in temperature

and precipitation and that migration responses differ signiﬁcantly

between countries and between rural and urban areas (Gray and Wise,

2016; Mueller etal., 2020). Multi-directional ﬁndings such as these are

also common in single-country studies from multiple regions (Call etal.,

2017; Nawrotzki etal., 2017; Cattaneo etal., 2019; Kaczan and Orgill-

Meyer, 2020). The diversity of potential migration and displacement

outcomes reﬂects (a) the variable nature of climate hazards in terms

of the rate of onset, intensity, duration, spatial extent and severity of

damage caused to housing, infrastructure and livelihoods and (b) the

wide range of social, economic, cultural, political and other non-climatic

factors that inﬂuence exposure, vulnerability, adaptation options

and the contexts in which migration decisions are made (Neumann

and Hermans, 2015; McLeman, 2017; Barnett and McMichael, 2018;

Cattaneo etal., 2019; Hoffmann etal., 2020) (high conﬁdence).

Weather events and climate conditions can act as direct drivers of

migration and displacement (e.g., destruction of homes by tropical

cyclones) and as indirect drivers (e.g., rural income losses and/or

food insecurity due to heat- or drought-related crop failures that

in turn generate new population movements) (high conﬁdence).

Extreme storms, ﬂoods and wildﬁres are strongly associated with

high levels of short- and long-term displacement, while droughts,

extreme heat and precipitation anomalies are more likely to stimulate

longer-term changes in migration patterns (Kaczan and Orgill-Meyer,

2020; Hoffmann et al., 2020). Longer-term environmental changes

attributable to anthropogenic climate change—such as higher

average temperatures, desertiﬁcation, land degradation, biodiversity

loss and sea level rise—have had observed effects on migration

and displacement in a limited number of locations in recent decades

but are projected to have wider-scale impacts on future population

patterns and migration, and are therefore assessed in Section7.3.2

(Projected Risks).

The diversity of potential migration and displacement outcomes

reﬂects the scale and physical impacts of speciﬁc climate hazard

events and the wide range of social, economic, cultural, political

and other non-climatic factors that inﬂuence exposure, vulnerability,

adaptation options and the contexts in which migration decisions are

made (high conﬁdence). The diversity in drivers, contexts and outcomes

makes it difﬁcult to offer simple generalisations about the relationship

between climate change and migration. The characteristics of climatic

drivers vary in terms of the rate of onset, intensity, duration, spatial

extent and severity of damage caused to housing, infrastructure and

livelihoods; the potential migration responses to these are further

mediated by cultural, demographic, economic, political, social and

other non-climatic factors operating across multiple scales (Neumann

and Hermans, 2015; McLeman, 2017; Barnett and McMichael, 2018;

Cattaneo etal., 2019; Hoffmann etal., 2020).

Climate-related migration and displacement outcomes display high

variability in terms of migrant success, often reﬂecting pre-existing

socioeconomic conditions and household wealth (high conﬁdence).

The decision to migrate or remain in place when confronted by

climatic hazards is strongly inﬂuenced by the range and accessibility

of alternative, in situ (i.e., non-migration) adaptation options that may

be less costly or disruptive (Cattaneo etal., 2019). Migration decisions

(whether climate-related or not) are typically made at the individual or

household level and are inﬂuenced by a household’s perceptions of risk,

social networks, wealth, age structure, health and livelihood choices

(Koubi etal., 2016b; Gemenne and Blocher, 2017). Households with

greater ﬁnancial resources and higher levels of educational attainment

have greater capacity to adapt in situ (Cattaneo and Massetti, 2019;

1080

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Cross-Chapter BoxMIGRATE | Climate-Related Migration

Authors: David Wrathall (USA, Chapter 8), Robert McLeman (Canada, Chapter 7), Helen Adams (United Kingdom, Chapter 7), Ibidun

Adelekan (Nigeria, Chapter 9), Elisabeth Gilmore (USA/Canada, Chapter 14), François Gemenne (Belgium, Chapter 8), Nathalie Hilmi

(Monaco, Chapter 18), Ben Orlove (USA, Chapter 17), Ritwika Basu (India/United Kingdom, Chapter 18), Halvard Buhaug (Norway,

Chapter 16), Edwin Castellanos (Guatemala, Chapter 12), David Dodman (United Kingdom, Chapter 6), Felix Kanungwe Kalaba (Zambia,

Chapter 9), Rupa Mukerji (Switzerland/India, Chapter 18), Karishma Patel (USA, Chapter 1), Chandni Singh (India, Chapter 10), Philip

Thornton (United Kingdom, Chapter 5), Christopher Trisos (South Africa, Chapter 9), Olivia Warrick (New Zealand, Chapter 15), Vishnu

Pandey (Nepal, Chapter 4)

Key messages on migration in this report

Migration is a universal strategy that individuals and households undertake to improve well-being and livelihoods in response to

economic uncertainty, political instability and environmental change (high conﬁdence). Migration, displacement and immobility that

occur in response to climate hazards are assessed in general in Chapter 7, with speciﬁc sectoral and regional dimensions of climate-

related migration assessed in sectoral and regional Chapters 5 to 15 (TableMIGRATE.1 in Chapter 7) and involuntary immobility and

displacement being identiﬁed as representative key risks in Chapter 16 (Sections16.2.3.8, 16.5.2.3.8). Since AR5 there has been a

considerable expansion in research on climate–migration linkages, with ﬁve key messages from the present assessment report warranting

emphasis.

Climatic conditions, events and variability are important drivers of migration and displacement (high conﬁdence) (TableMIGRATE.1 in

Chapter 7), with migration responses to speciﬁc climate hazards being strongly inﬂuenced by economic, social, political and demographic

processes (high conﬁdence) (Sections7.2.6, 8.2.1.3). Migration is among a wider set of possible adaptation alternatives and often emerges

when other forms of adaptation are insufﬁcient (Sections5.5.1.1, 5.5.3.5, 7.2.6, 8.2.1.3, 9.7.2). Involuntary displacement occurs when

adaptation alternatives are exhausted or not viable and reﬂects non-climatic factors that constrain adaptive capacity and create high

levels of exposure and vulnerability (high conﬁdence) (Cross-Chapter BoxSLR in Chapter 3; Sections4.3.7, 7.2.6; Box8.1; Section10.3;

Box14.7). There is strong evidence that climatic disruptions to agricultural and other rural livelihoods can generate migration (high

conﬁdence) (Sections5.5.4, 8.2.1.3, 9.8.3; Box9.8).

Speciﬁc climate events and conditions may cause migration to increase, decrease or ﬂow in new directions (high conﬁdence), and the

more agency migrants have (i.e., the degree of voluntarity and freedom of movement), the greater the potential beneﬁts for sending and

receiving areas (high agreement, medium evidence) (Sections5.5.3.5, 7.2.6, 8.2.1.3; Box12.2). Conversely, displacement or low-agency

migration is associated with poor outcomes in terms of health, well-being and socioeconomic security for migrants and returns fewer

beneﬁts to sending or receiving communities (high agreement, medium evidence) (Sections4.3.7, 4.5.7; Box8.1; Sections9.7.2, 10.3;

Box14.7).

Most climate-related migration and displacement observed currently takes place within countries (high conﬁdence) (Sections4.3.7,

4.5.7, 5.12.2, 7.2.6). The climate hazards most commonly associated with displacement are tropical cyclones and ﬂooding in most

regions, with droughts being an important driver in sub-Saharan Africa, parts of south Asia and South America (high conﬁdence)

(Sections7.2.6.1, 9.7.2, 10.4.6.3, 11.4.1, 12.5.8.4, 13.8.1.3, 14.4.7.3). Currently, observed international migration associated with

climatic hazards is considerably smaller relative to internal migration and is most often observed as ﬂowing between states that are

contiguous and have labour-migration agreements and/or longstanding cultural ties (high agreement, robust evidence) (Sections4.3.7,

4.5.7, 5.12.2, 7.2.6).

In many regions, the frequency and/or severity of ﬂoods, extreme storms and droughts is projected to increase in coming decades,

especially under high-emissions scenarios (WGI AR6 Chapter 12 (Ranasinghe etal. 2021)), raising future risk of displacement in the most

exposed areas (high conﬁdence) (Section7.3.2.1). The additional impacts of climate change anticipated to generate future migration and

displacement include mean sea level rise that increases ﬂooding and saltwater contamination of soil and/or groundwater in low-lying

coastal areas and small islands (high conﬁdence) (Section7.3.2.1; Cross-Chapter BoxSLR in Chapter 3) and more frequent extreme heat

Ocello et al., 2015) but are also better able to migrate and with

greater agency once such a decision is made (Kubik and Maurel, 2016;

Koubi etal., 2016b; Riosmena etal., 2018; Adams and Kay, 2019). By

contrast, poor households with limited physical, social and ﬁnancial

resources have less capacity to adapt in situ and are often limited in

their migration options (Nawrotzki and DeWaard, 2018; Suckall etal.,

2017; Zickgraf etal., 2016). Thus, when poorer households do migrate

after an extreme climate event, it is often in reaction to lost income or

livelihood and occurs with low voluntarity (Mallick etal., 2017; Bhatta

etal., 2015) and may perpetuate or amplify migrants’ socioeconomic

precarity and/or their exposure to environmental hazards (Natarajan

etal., 2019; see also Section8.3.1).

1081

Health, Wellbeing and the Changing Structure of Communities Chapter 7

events that threaten the habitability of urban centres in the tropics and arid/semiarid regions (medium conﬁdence), although the causal

links between heat and migration are less clear (Section7.3.2.1).

There is growing evidence about the future prospects of immobile populations: groups and individuals that are unable or unwilling to

move away from areas highly exposed to climatic hazards (high conﬁdence) (Sections4.6.9, 7.2.6.2; Box8.1; Box10.2). Involuntarily

immobile populations may be anticipated to require government interventions to continue living in exposed locations or to relocate

elsewhere (high agreement, medium evidence) (Box8.1). Managed retreat and organised relocations of people from hazardous areas

in recent years have proven to be politically and emotionally charged, socially disruptive and costly (high conﬁdence) (Section7.4.5.4).

Climate-migration interactions and outcomes

FigureMIGRATE.1 in Chapter 7 presents a simpliﬁed framework for understanding how migration and displacement may emerge from

the interactions of climatic and non-climatic factors, based on the characteristic risk framework introduced in Chapter 1 (Section1.3).

Voluntary migration can be used by households when adapting to climate hazards, while less voluntary forms of migration and displacement

emerge when other forms of adaptation (referred to in FigureMIGRATE.1 in Chapter 7 as in situ adaptation) are inadequate. Migration

outcomes—expressed in FigureMIGRATE.1 in Chapter 7 as changes in future risks to the well-being of migrants, sending communities

and destination communities—are heavily inﬂuenced by the political, legal, cultural and socioeconomic conditions under which migration

occurs. Groups and individuals that are involuntarily immobile may ﬁnd that their exposure, vulnerability and risk increase over time.

TableMIGRATE.1 in Chapter 7 summarises the range of potential migration outcomes that may emerge from this dynamic and indicates

speciﬁc sections in sectoral and regional chapters of the report that describe examples of each.

Climate-migration processes and outcomes

In SITU

Adaptation

Vulnerability

Exposure

Response

Hazard

Risk

decreases

Risk

increases

Climate Factors

Non-Climate Factors

Threshold(s)

MIGRATION OUTCOMES

See Table CCB 1

Immobility

FigureMIGRATE.1 | General interactions between climatic and non-climatic processes, adaptation, potential migration outcomes and implications

for future risk. Adapted from McLeman etal. (2021).

Cross-Chapter BoxMIGRATE (continued)

1082

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

TableMIGRATE.1 | Typology of climate-related migration and examples in sectoral and regional chapters of AR6.

Type of climate-re-

lated migration

Characteristics

Recent or current

examples

Examples in the literature References in AR6 WGII

Temporary and/or

seasonal migration

Frequently used as a risk-reduction

strategy by rural households in

less-developed regions with highly

seasonal precipitation; includes

transhumance

Pastoralists in

sub-Saharan Africa;

seasonal farm workers in

south Asia; rural–urban

labour migration in

Central America

Aﬁﬁ etal. (2016); Call etal.

(2017); Piguet etal. (2018);

Borderon etal. (2019); Cattaneo

etal. (2019); Hoffmann etal.

(2020); Lopez-i-Gelats etal.

(2015); Lu etal. (2016) Kaczan

and Orgill-Meyer (2020)

Sections5.5.1.1, 5.5.3.5;

Section7.2.6; Section8.2.1.3;

Section9.8.3; Box13.2

Indeﬁnite or permanent

migration

Less common than temporary or seasonal

migration, particularly when the whole

household permanently relocates

Numerous examples in all

regions

See reviews listed in cell above

Section7.2.6; Section8.2.1.3;

Box10.2

Internal migration

Movements within state borders; most

common form of climate-related migration

Numerous examples in all

regions

See reviews listed in cell above

Section4.3.7; Sections5.5.4,

5.10.1.1; Section7.2.6;

Sections9.7.2, 9.11; Box9.8;

Sections10.3.3, 10.4.6.3,

Box10.2; Section11.4.1;

Section12.5.8.4; Section13.8.1.3;

Section14.4.7.3; Section15.3.4.6

International migration

Less common than internal migration;

most often occurs between contiguous

countries within the same region; often

undertaken for purpose of earning wages

to remit home

Cross-border migration

within south and

Southeast Asia,

sub-Saharan Africa

See reviews listed in cell above;

also Veronis etal. (2018);

McLeman (2019); Cattaneo and

G. (2016); Missirian and Schlenker

(2017); Schutte etal. (2021)

Sections4.3.7, 4.5.7;

Section5.12.2; Section7.2.6

Rural–urban or rural–

rural

Typically internal but may also ﬂow

between contiguous states; may be

for temporary or indeﬁnite periods;

migration may be undertaken by an

individual household member or the

entire household; may be followed by

remittances

Drought migration in

Mexico, east Africa and

south Asia

See reviews in the cell above;

also Adger etal. (2015); Gautier

etal. (2016); Nawrotzki etal.

(2017); Wiederkehr etal. (2018);

Robalino etal. (2015); Borderon

etal. (2019); Murray-Tortarolo and

Martnez (2021)

Section5.13.4; Section7.2.6;

Section6.2.4.3; Section8.2.1.3;

Section9.8.1.2; Section12.5.8.4;

Section14.4.7.1

Displacement

Households are forced to leave homes for

temporary or indeﬁnite period; typically

occurs as a result of extreme events

and starts with seemingly temporary

evacuation; risk is expected to rise in most

regions due to sea level rise and changes

in associated coastal hazards

Tropical cyclones in the

Caribbean, Southeast Asia

and Bay of Bengal region

Islam and Shamsuddoha (2017);

Desai etal. (2021); see Internal

Displacement Monitoring Centre

annual reports for global statistics

Cross-Chapter BoxSLR in

Chapter 3; Section4.3.7; 4.5.7;

Cross-Chapter BoxMOVING PLATE

in Chapter 5; Section7.2.6.1;

Box8.1; Section9.7.2;

Section9.9.2; Section10.3;

Box14.7; Sections15.3.4.6;

CCP2.2.2

Planned and/or

organised resettlement

Initiated in areas where settlements

become permanently uninhabitable;

requires assistance from governments

and/or institutions; government-sponsored

sedentarisation of pastoral populations

Fiji, Carteret Islands,

Papua New Guinea, Gulf

of Mexico coast and

coastal Alaska, USA

Marino and Lazrus (2015);

Hino etal. (2017); McNamara

etal. (2018); McMichael and

Katonivualiku (2020); Tadgell etal.

(2017); Arnall (2014); Wilmsen

and Webber (2015)

Section4.6.9; Sections5.14.1,

5.14.2; Section7.4.4.4;

Section10.4.6; Section15.5.3;

CCP2.2.2; CCP6.3.2

Immobility

Adverse weather or climatic conditions

warrant moving, but households are

unable to relocate because of lack of

resources or choose to remain because

of strong social, economic or cultural

attachments to place

Examples in most regions

Adams (2016); Zickgraf (2018);

Nawrotzki and DeWaard (2018);

Farbotko etal. (2020)

Section4.6.9; Section7.2.6.2;

Box8.1; Box10.2

Policy implications

Future migration and displacement patterns in a changing climate will depend not only on the physical impacts of climate change, but

also on future policies and planning at all scales of governance (high conﬁdence) (4.6.9, 5.14.1, 5.14.1.2, 7.3.2, 7.4.4, 8.2.1.3; Box8.1;

CCP6.3.2). Policy interventions can remove barriers to and expand the alternatives for safe, orderly and regular migration that allows

vulnerable people to adapt to climate change (high conﬁdence) (Section7.2.6). With adequate policy support, migration in the context

Cross-Chapter BoxMIGRATE (continued)

1083

Health, Wellbeing and the Changing Structure of Communities Chapter 7

of climate change can result in synergies for both adaptation and development (Sections5.12.2, 7.4.4, 8.2.1.3). Migration governance at

local, national and international levels will inﬂuence the outcomes of climate-related migration for the migrants themselves as well as for

receiving and origin communities (Sections5.13.4, 7.4.4, 8.2.1.3). At the international level, a number of relevant policy initiatives and

agreements, including Global Compacts for Safe, Orderly and Regular Migration and for the protection of Refugees; the Warsaw

International Mechanism of the UNFCCC; the Sustainable Development Goals; the Sendai Framework for Disaster Risk Reduction; and the

Platform on Disaster Displacement, have already been established, merit continued pursuit and provide potential migration governance

pathways (Section7.4.4). Policy and planning decisions at regional, national and local scales that relate to housing, infrastructure, water

provisioning, schools and healthcare are relevant for successful integration of migrants into receiving communities (Sections5.5.4,

5.10.1.1, 5.12.2, 9.8.3). Policies and practices on movements of people across international borders are also relevant to climate-related

migration, with restrictions on movement having implications for the adaptive capacity of communities exposed to climate hazards

(Section7.4.4.2; Box8.1). Perceptions of migrants and the framing of policy discussions in receiving communities and nations are

important determinants of the future success of migration as an adaptive response to climate change (Section7.4.4.3) (high agreement,

medium evidence).

Reducing the future risk of large-scale population displacements, including those requiring active humanitarian interventions and

organised relocations of people, requires the international community to meet the requirements of the Paris Agreement and take further

action to control future warming (high conﬁdence) (Cross-Chapter BoxSLR in Chapter 3; Section7.3.1; Box8.1). Current emissions

pathways lead to scenarios for the period between 2050 and 2100 in which hundreds of millions of people will be at risk of displacement

due to rising sea levels, ﬂoods, tropical cyclones, droughts, extreme heat, wildﬁres and other hazards, with land degradation exacerbating

these risks in many regions (Section7.3.2; IPCC 2019b; Cross-Chapter BoxSLR in Chapter 3). At high levels of warming, tipping points may

exist, particularly related to sea level rise, that, if crossed, would further increase the global population potentially at risk of displacement

(Ranasinghe etal. 2021). Populations in low-income countries and small-island states that have historically had low greenhouse gas

(GHG) emissions are at particular risk of involuntary migration and displacement due to climate change, reinforcing the urgency for

industrialised countries to continue lowering GHG emissions, to support adaptive capacity-building initiatives under the UNFCCC and to

meet objectives expressed in the Global Compacts regarding safe, orderly and regular migration and the support and accommodation of

displaced people (Sections4.3.7, 4.5.7, 5.12.2, 7.4.5.5, 8.4.2; Box8.1; Cross-Chapter BoxSLR in Chapter 3).

Cross-Chapter BoxMIGRATE (continued)

Climate-related migration originates most often in rural areas in

low- and middle-income countries, with migrant destinations usually

being other rural areas or urban centres within their home countries

(i.e., internal migration) (medium conﬁdence). Rural livelihoods and

incomes based on farming, livestock rearing and/or natural resource

collection are inherently sensitive to climate variability and change,

creating greater potential for migration as a response (Bohra-Mishra

et al., 2017; Viswanathan and Kumar, 2015). Drought events have

been associated with periods of higher rural to urban migration

within Mexico (Chort and de la Rupelle, 2016; Leyk et al., 2017;

Nawrotzki et al., 2017; Murray-Tortarolo and Martnez, 2021) and

Senegal (Nawrotzki and Bakhtsiyarava, 2017). Extreme temperatures

are associated with higher rates of temporary rural out-migration in

South Africa and in Bangladesh (Mastrorillo etal., 2016; Call etal.,

2017). In rural Tanzania, weather-related shocks to crop production

have been observed to increase the likelihood of migration but

typically only for households in the middle of community wealth

distribution (Kubik and Maurel, 2016). Weather-related losses in rice

production have been associated with small-percentage increases

in internal migration in India (Viswanathan and Kumar, 2015) and

the Philippines (Bohra-Mishra etal., 2017). In east Africa, temporary

rural–urban labour migration does not show a strong response to

climatic drivers (Mueller etal., 2020). There is limited literature on

mobility as adaptation in urban populations, with the focus being on

resettlement of ﬂood-prone informal settlements within cities (Kita,

2017; Tadgell etal., 2017).

Most documented examples of international climate-related migration

are intra-regional movements of people between countries with shared

borders (high agreement, medium evidence). Systematic reviews ﬁnd

few documented examples of long-distance, inter-regional migration

driven by climate events (Veronis et al., 2018; Kaczan and Orgill-

Meyer, 2020; Hoffmann et al., 2020). One macro-economic analysis

found a correlation between migrant ﬂows from low- to high-income

countries and adverse climatic events in the source country (Coniglio

and Pesce, 2015). Another study found that high heat stimulates higher

rates of international migration from middle-income countries but

typically not from low-income countries (Cattaneo and Peri., 2016),

while other studies found international climate-related migration

originates primarily from agriculture-dependent countries (Cai etal.,

2016; Nawrotzki and Bakhtsiyarava, 2017). Small-sample studies of

migrants to Canada from Bangladesh, Haiti and sub-Saharan Africa

suggest environmental factors in the source country can be a primary

or secondary motivation for some migrants within larger ﬂows of

economic and family-reuniﬁcation migrants (Veronis and McLeman,

2014; Mezdour et al., 2015; McLeman et al., 2017). Research on

the links between climate hazards and international movements of

refugees and/or asylum seekers shows differing results. One study

found that asylum applications in Europe increase during climate

ﬂuctuations due to interactions with conﬂict (Missirian and Schlenker,

2017), and another found links between heat, drought, conﬂict and

asylum-seeking migration originating in the Middle East between

2011 and 2015 (Abel et al., 2019). Other studies have found that

1084

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Average annual weather-related displacements, 2010–2020

Wet mass movement

Mass movement

Extreme temperatures

Drought

Wildfire

Storm

Flood

C. & S.

America

Small

Islands

Europe Australasia

Millions

of people

displaced

Weather-related hazards

Expanded inset

Asia Africa

North

America

Central

and South

America

Small

Islands

Europe Australasia

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure7.7 | Average number of people displaced annually by selected weather-related events from 2010 to 2020 by region. See text for important notes

regarding data collection and trends. Source statistics provided by the Internal Displacement Monitoring Centre (https://www.internal-displacement.org/).

asylum claims in Europe correspond minimally with climatic hazards

in source countries (Schutte etal., 2021), with choices in baseline data,

timeframes for analysis and methodological approaches potentially

explaining the inconsistent results across studies (Boas etal., 2019).

Media reports and other studies in recent years suggest that climate

change has driven large numbers of migrants to the US from Central

America and to Europe from the Middle East and Africa, but empirical

studies were not identiﬁed for this assessment.

7.2.6.1 Relative Importance of Speciﬁc Climatic Drivers of

Migration and Displacement

Reliable global estimates of voluntary climate-related migration

within and between countries are not available due to a general

absence of data of this speciﬁc nature, with existing national and

global datasets often lacking information on migration causation or

motivation. Better data are available for involuntary displacements

within countries for reasons associated with weather-related hazards.

Data collected annually since 2008 on internal displacements

attributed to extreme weather events by the IDMC indicate that

extreme storms and ﬂoods are the two most signiﬁcant weather-

related drivers of population displacements globally. Because of

improvements in collection sources and methods since it ﬁrst began

reporting data in 2008, upward trends since that year in the total

reported annual number of people displaced should be treated

cautiously. However, it is reasonable to conclude that the average

annual rate currently exceeds 20 million people globally, with

considerable interannual variation due to the frequency and severity

of extreme events in heavily populated areas. Regional distribution

of displacement events has been consistent throughout the IDMC

data collection period (high conﬁdence), with displacement events

occurring most often in East, Southeast and south Asia; sub-Saharan

Africa; the USA; and the Caribbean region (Figure 7.7). Relative

to their absolute population size, small island states experience a

disproportionate risk of climate-related population displacements

(Desai etal., 2021) (high conﬁdence).

Tropical cyclones and extreme storms are a particularly signiﬁcant

displacement risk in East and Southeast Asia, the Caribbean region,

the Bay of Bengal region and southeast Africa (IDMC 2020) (high

conﬁdence). The scale of immediate displacement from any given

storm and potential for post-event migration depend heavily on the

extent of damage to housing and livelihood assets and the responsive

capacity of governments and humanitarian relief agencies (Saha,

2016; Islam et al., 2018; Mahajan, 2020; Spencer and Urquhart,

2018). In Bangladesh, the rural poor are most often displaced, with

initial increases in short-term, labour-seeking migration followed by

more permanent migration by some groups (Saha, 2016; Islam and

Hasan, 2016; Islam and Shamsuddoha, 2017). Past hurricanes in the

Caribbean basin have generated internal and inter-state migration

within the region, typically along pre-existing social networks,

and to the USA (Loebach, 2016; Chort and de la Rupelle, 2016). In

2017, Hurricanes Irma and Maria caused widespread damage to

infrastructure and health services, and a slow recovery response by

authorities was followed by the migration of tens of thousands of

Puerto Ricans to Florida and New York (Zorrilla, 2017; Echenique and

Melgar, 2018). In the US, coastal counties experience increased out-

migration after hurricanes that ﬂows along existing social networks

(Hauer, 2017), with post-disaster reconstruction employment

opportunities potentially attracting new labour migrants to affected

1085

Health, Wellbeing and the Changing Structure of Communities Chapter 7

areas (Ouattara and Strobl, 2014; Curtis etal., 2015; DeWaard etal.,

2016; Fussell etal., 2018).

Riverine ﬂood displacement can lead to increases or decreases in

temporary or short-distance migration ﬂows, depending on the local

context (medium conﬁdence) (Robalino et al., 2015; Ocello et al.,

2015; Aﬁﬁ etal., 2016; Koubi etal., 2016b). Floods are a particularly

important driver of displacement in river valleys and deltas in Asia and

Africa, although large ﬂood-related displacements have been recorded

by the IDMC in all regions. In Africa, populations exposed to low ﬂood

risks, as compared with other regions, are observed to have a greater

vulnerability to displacement due to limited economic resources and

adaptive capacity (Kakinuma etal., 2020). In areas where ﬂooding is

especially frequent, in situ adaptations may be more common, and

out-migration may temporarily decline after a ﬂood (Aﬁﬁ etal., 2016;

Chen etal., 2017; Call etal., 2017). Rates of indeﬁnite or permanent

migration tend not to change following riverine ﬂoods unless damage

to homes and livelihood assets is especially severe and widespread,

with household perceptions of short- and longer-term risks playing an

important role (Koubi etal., 2016a).

Displacements due to droughts, extreme heat and associated impacts on

food and water security are most frequent in east Africa and, to a lesser

extent, south Asia and west and southern Africa (IDMC, 2020). Since

droughts unfold progressively and typically do not cause permanent

damage to housing or livelihood assets, there is greater opportunity for

government and non-governmental organisation (NGO) interventions

and greater use of in situ adaptation options (Koubi et al., 2016b;

Koubi etal., 2016a; Cattaneo etal., 2019). Drought-related population

movements are most common in dryland rural areas of low-income

countries and occur after a threshold is crossed and in situ adaptation

options are exhausted (Gautier etal., 2016; Wiederkehr etal., 2018;

McLeman, 2017). Observed population movements may occur for an

extended period after the event; one study of Mexican data found this

lag to be up to 36months (Nawrotzki etal., 2017). The most common

response to drought is an increase in short-distance, rural–urban

migration (medium conﬁdence), with examples being documented

in Bangladesh, Ethiopia, Pakistan, sub-Saharan Africa, Latin America

and Brazil (Neumann and Hermans, 2015; Gautier etal., 2016; Gautier

etal., 2016; Mastrorillo etal., 2016; Baez etal., 2017; Call etal., 2017;

Nawrotzki etal., 2017; Jessoe etal., 2018; Carrico and Donato, 2019;

Hermans and Garbe, 2019).

Few assessable studies were identiﬁed that examine links between

wildﬁres and migration. Wildﬁre events are often associated with

urgent evacuations and temporary relocations, which place signiﬁcant

stress on receiving communities (Spearing and Faust., 2020), but

research in the USA suggests ﬁres have only a modest inﬂuence on

future migration patterns in exposed areas (Winkler and Rouleau.,

2021). More research, particularly in other regions, is needed.

7.2.6.2 Immobility and Resettlement in the Context of Climatic

Risks

Immobility in the context of climatic risks can reﬂect vulnerability and

lack of agency (an inability to migrate), but can also be a deliberate

choice (high conﬁdence). Research since AR5 shows that immobility

is best described as a continuum from people who are ﬁnancially or

physically unable to move away from hazards (involuntary immobility)

to people who choose not to move (voluntary immobility) because

of strong attachments to place, culture and people (Nawrotzki and

DeWaard, 2018; Adams, 2016; Farbotko and McMichael, 2019; Zickgraf,

2019; Neef etal., 2018; Suckall etal., 2017; Ayeb-Karlsson etal., 2018;

Zickgraf, 2018; Mallick and Schanze, 2020). Involuntary immobility is

associated with individuals and households with low adaptive capacity

and high exposure to hazard, and can exacerbate inequality and future

Box7.4 | Gender Dimensions of Climate-Related Migration

Migration decision-making and outcomes—in both general terms and in response to climatic risks—are strongly mediated by gender,

social context, power dynamics and human capital (Bhagat, 2017; Singh and Basu, 2020; Rao etal., 2019a; Ravera etal., 2016). Women

tend to suffer disproportionately from the negative impacts of extreme climate events for reasons ranging from caregiving responsibilities

to lack of control over household resources to cultural norms for attire (Belay etal., 2017; Jost etal., 2016). In many cultures, migrants

are most often able-bodied, young men (Call etal., 2017; Heaney and Winter, 2016). Womenwait longer to migrate because of higher

social costs and risks (Evertsen and Van Der Geest, 2019) and barriers such as social structures, cultural practices, lack of education and

reproductive roles (Belay etal., 2017; Afriyie etal., 2018; Evertsen and Van Der Geest, 2019).

Research critiques the tendency to portray women as victims of climate hazards rather than recognising differences between women and

the potential for women to use their agency and informal networks to negotiate their situations (Eriksen etal., 2015; Ngigi etal., 2017;

Pollard etal., 2015; Rao etal., 2019b; Ravera etal., 2016). Migration can change household composition and structure, which in turn

affects the adaptive capacity and choices of those who do not move (Rao etal., 2019a; Rao etal., 2019b; Singh, 2019). For example, when

only male household members move, the remaining members of the now female-headed household must take on greater workloads

(Goodrich etal., 2019; Rao etal., 2019b; Rigg and Salamanca, 2015), leading to increased workload and greater vulnerability for those

left behind (Arora etal., 2017; Bhagat, 2017; Flatø etal., 2017; Lawson etal., 2019). It can, however, also increase women’s economic

freedom and decision-making capacity, enhance their agency (Djoudi etal., 2016; Rao, 2019) and alter the gendered division of paid

work, care and intra-household relations (Rigg etal., 2018; Singh and Basu, 2020), a process that may reduce household vulnerability to

extreme climate events (Banerjee etal., 2019b).

1086

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

vulnerability to climate change (Sheller, 2018), including through

impacts on health (Schwerdtle et al., 2018). Voluntary immobility

represents an assertion of the importance of culture, livelihoods and

people to well-being, and is of particular relevance for Indigenous

Peoples (Suliman etal., 2019).

Planned relocations by governments of settlements and populations

exposed to climatic hazards are not presently commonplace,

although the need is expected to grow in coming decades (Hino

et al 2017). Examples include relocations of coastal settlements

exposed to storm and erosion hazards as well as smaller numbers

of cases of ﬂood-prone settlements in river valleys; these examples

suggest that organised relocations are expensive, contentious, create

multiple challenges for governments and generate short- and longer-

term disruptions for the people involved (high agreement, medium

evidence) (Ajibade etal., 2020; Henrique and Tschakert, 2020; Desai

etal., 2021).

Examples of relocations of small indigenous communities in coastal

Alaska and villages in the Solomon Islands and Fiji suggest that

relocated people experience signiﬁcant ﬁnancial and emotional distress

as cultural and spiritual bonds to place and livelihoods are disrupted

(Albert etal., 2018; Neef etal., 2018; McMichael and Katonivualiku,

2020; McMichael and Katonivualiku, 2020; McMichael et al., 2021;

Piggott-McKellar et al., 2019; Bertana, 2020). Voluntary relocation

programmes offered by US state governments in communities damaged

by Hurricane Sandy in 2012 have been subject to multiple studies, and

these show longer-term economic outcomes, social connections and

mental well-being vary for a range of reasons unrelated to the impacts

of the hazard event itself (Bukvic and Owen, 2017; Binder etal., 2019;

Koslov and Merdjanoff, 2021).

7.2.6.3 Connections Between Climate-Related Migration and

Health

The number of assessable peer-reviewed studies that make connections

between climate-related migration and health and well-being is small.

The health outcomes of migrants generally, and of climate-migrants

in particular, vary according to geographical context, country and the

particular circumstances of migration or immobility (Hunter and Simon,

2017; Hunter et al., 2021; Schwerdtle et al., 2020). Such linkages are

‘multi-directional’, with studies suggesting that healthy individuals

may be more likely to migrate internationally in search of economic

opportunities than people in poorer health, except during adverse

climatic conditions when migration rates may change across all groups,

and that migrants may have different long-term health outcomes than

people born in destination areas, potentially displaying a range of positive

and negative health outcomes compared to non-migrants (Kennedy

etal., 2015; Dodd etal., 2017; Hunter and Simon, 2017; Riosmena etal.,

2017). Refugees and other involuntary migrants often experience higher

exposure to disease and malnutrition, adverse indirect health effects of

changes in diet or activity and increased rates of mental health concerns.

These latter may be attributable to a sense of loss or fear (Schwerdtle

etal., 2018; Torres and Casey, 2017) as well as due to the interruption

of healthcare; occupational injuries; sleep deprivation; non-hygienic

lodgings and insufﬁcient sanitary facilities; heightened exposure to

vector- and WBDs; vulnerability to psychosocial, sexual and reproductive

issues; behavioural disorders; substance abuse; and violence (Farhat etal.,

2018; Wickramage et al., 2018) (high conﬁdence). Linkages between

climate migration and the spread of infectious disease are bidirectional;

migrants may be exposed to diseases at the destination to which they

have lower immunity than the host community; in other cases, migrants

could introduce diseases to the receiving community (McMichael, 2015).

Thus, receiving areas may have to pay greater attention to building

migrant sensitive health systems and services (Hunter and Simon, 2017).

The risk of migration leading to disease transmission is exacerbated by

weak governance and lack of policy to support public health measures

and access to medicines (Pottie etal., 2015).

7.2.7 Observed Impacts of Climate on Conﬂict

7.2.7.1 Introduction

In AR5, conﬂict was addressed in WGII Chapter 12 on human security.

The chapter concluded that some of the factors that increase the risk of

violent conﬂict within states are sensitive to climate change (medium

evidence, medium agreement), that people living in places affected by

violent conﬂict are particularly vulnerable to climate change (medium

conﬁdence) and that climate change will lead to new challenges to

states and will increasingly shape both conditions of security and

national security policies (medium evidence, medium agreement). AR5

characterised a major debate within the ﬁeld as: authors supporting

an association between climate anomalies and conﬂict that can be

extrapolated into the future (e.g., Hsiang etal. (2013); Hsiang and

Marshall (2014); Burke etal. (2015a)) and authors arguing that these

associations are not universal and break down when contextual, scale

and political factors are introduced (e.g., Buhaug etal. (2014); Buhaug

(2016)).

Consistent with AR5 ﬁndings, there continues to be little observed

evidence that climatic variability or change cause violent inter-state

conﬂict. In intra-state settings, climate change has been associated

with the onset of conﬂict, civil unrest or riots in urban settings (high

agreement, medium evidence) (Ide (2020), and changes in the duration

and severity of existing violent conﬂicts (Koubi, 2019). Climate change

is conceptualised as one of many factors that interact to raise tensions

(Boas and Rothe, 2016) through diverse causal mechanisms (Mach

etal., 2019; Ide etal., 2020) and as part of the peace-vulnerability-

development nexus (Barnett, 2019; Abrahams, 2020; Buhaug and von

Uexkull, 2021). New areas of the literature assessed in this report

include the security implications of responses to climate change, the

gendered dynamics of conﬂict and exposure to violence under climate

change and civil unrest in urban settings. The impact of violent conﬂict

on vulnerability is not addressed in this chapter but does arise in

other chapters (Sections 8.3.2.3, 17.2.2.2). Other chapters address

non-violent conﬂict over changing availability and distribution of

resources, for example, competing land uses and ﬁsh stocks migrating

to different territories (Sections 5.8.2.3; 5.8.3, 5.9.3, 5.13, 9.8.1.1,

9.8.5.1). A commonly used deﬁnition of armed conﬂict is conﬂicts

involving greater than 25 battle-related deaths in a year; this number

represents the Uppsala Conﬂict Data Program threshold for inclusion

in their database, a core resource in this ﬁeld.

1087

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Climatic conditions have affected armed conﬂict within countries, but

their inﬂuence has been small compared to socioeconomic, political

and cultural factors (Mach et al., 2019) (high agreement, medium

evidence). Inter-group inequality, and consequent relative deprivation

can lead to conﬂict and the negative impacts of climate change

lower the opportunity cost of involvement in conﬂict (Buhaug etal.,

2020; Vestby, 2019). Potential pathways linking climate and conﬂict

include direct impacts on physiology from heat or resource scarcity;

indirect impacts of climatic variability on economic output, agricultural

incomes, higher food prices and increasing migration ﬂows; and the

unintended effects of climate mitigation and adaptation policies (Koubi,

2019; Busby, 2018; Sawas etal., 2018). Relative deprivation, political

exclusion and ethnic fractionalisation and ethnic grievances are other

key variables (Schleussner etal., 2016; Theisen, 2017). Research shows

that factors such as land tenure and competing land uses interacting

with market-driven pressures and existing ethnic divisions produce

conﬂict over land resources rather than a scarcity of natural resources

caused by climate impacts such as drought (high agreement, medium

evidence) (Theisen, 2017; Balestri and Maggioni, 2017; Kuusaana and

Bukari, 2015; Box8.3).

7.2.7.2 Impacts of Climate Change and Violent Conﬂict

Positive temperature anomalies and average increases in temperature

over time have been associated with collective violent conﬂict in certain

settings (medium agreement, low evidence). Helman and Zaitchik

(2020) ﬁnd statistical associations between temperature and violent

conﬂict in Africa and the Middle East that are stronger in warmer places

and identify seasonal temperature effects on violence. However, they

are unable to detect the impact of regional temperature increases on

violence. For Africa, Van Weezel (2019) found associations between

average increases in temperature and conﬂict risk. Caruso etal. (2016)

found an association between rises in minimum temperature and

violence through the impact of temperature on rice yields (Box9.4).

However, the associations between temperature and violence are

weak compared to those with political and social factors (e.g., Owain

and Maslin (2018)) and research focuses on areas where conﬂict is

already present and, as such, is sensitive to selection bias (Adams etal.,

2018). There is a body of literature that ﬁnds statistical associations

between temperature anomalies and inter-personal violence, crime

and aggression in the Global North, predominantly in the USA (e.g.,

Ranson (2014); Mares and Moffett (2019); Tiihonen et al. (2017);

Parks etal. (2020); Section14.4.8). However, authors have cautioned

against extrapolating seasonal associations into long-term trends and

against focusing on individual crimes rather than wider social injustices

associated with climate change and its impacts (Lynch etal., 2020).

Variation in availability of water has been associated with international

political tension and intra-national collective violence (low agreement,

medium evidence). Drought conditions have been associated with

violence due to impacts on income from agriculture and water and

food security, with studies focusing predominantly on sub-Saharan

Africa and the Middle East (Ide and Frohlich, 2015; De Juan, 2015; Von

Uexkull etal., 2016; Waha etal., 2017; Abbott etal., 2017; D’Odorico

etal., 2018). A small set of published studies has argued inconclusively

over the role of drought in causing the Syrian civil war (Gleick, 2014;

Kelley etal., 2015; Selby etal., 2017; 16.2.3.9). In general, research

stresses the underlying economic, social and political drivers of

conﬂict. For example, research on conﬂict in the Lake Chad region

has demonstrated that the lake drying was only one of many factors

including lack of development and infrastructure (Okpara etal., 2016;

Nagarajan etal., 2018; Tayimlong, 2020). Fewer studies examine the

relationship between ﬂooding (excess water) and violence and often

rely on migration as the causal factor (see below). However, some

studies have shown an association between ﬂooding and civil unrest

(Ide etal., 2020; Section4.3.6; Section12.5.3; Box9.4).

Extreme weather events can be associated with increased conﬂict risk

(low agreement, medium evidence). There is the potential for extreme

weather events and disasters to cause political instability and increase

the risk of violent conﬂict, although not conclusively (Brzoska, 2018).

Post-disaster settings can be used to intensify state repression (Wood

and Wright, 2016) and to alter insurgent groups’ behaviour (Walch,

2018). Different stakeholders use disasters to establish new narratives

and alter public opinion (Venugopal and Yasir, 2017). Some research

has demonstrated how post-disaster activities have had positive

impacts on the social contract between people and the state, reducing

the risk of conﬂict by strengthening relations between government and

citizens and strengthening the citizenship of marginalised communities

(Siddiqi, 2018; Pelling and Dill, 2010; Siddiqi, 2019). However, post-

disaster and disaster risk-related activities themselves have limited

capacity to support diplomatic efforts to build peace (Kelman etal.,

2018).

7.2.7.3 Causal Pathways Between Climate Change Impacts and

Violent Conﬂict

Increases in food prices due to reduced agricultural production and

global food price shocks are associated with conﬂict risk and represent

a key pathway linking climate variability and conﬂict (medium

conﬁdence). Increases in food prices are associated with civil unrest

in urban areas among populations unable to afford or produce their

own food and in rural populations due to changes in availability of

agricultural employment with shifting commodity prices (Martin-

Shields and Stojetz, 2019). Under such conditions, locally speciﬁc

grievances, hunger and social inequalities can initiate or exacerbate

conﬂicts. Food price volatility in general is not associated with violence,

but sudden food price hikes have been linked to civil unrest in some

circumstances (Bellemare, 2015; McGuirk and Burke, 2020; Winne and

Peersman, 2019). In urban settings in Kenya, Koren etal. (2021) found

an association between food and water insecurity that is mutually

reinforcing and associated with social unrest (although insecurity in

either food or water on its own was not). Analysing the global food

riots in 2007/2008 and 2011, Heslin (2021) stresses the role of local

politics and pre-existing grievances in determining whether people

mobilise around food insecurity (Chapter 5).

Climate-related internal migration has been associated with the

experience of violence by migrants, the prolongation of conﬂicts in migrant

receiving areas and civil unrest in urban areas (medium agreement, low

evidence). Research points to the potential for conﬂict to serve as an

intervening factor between climate and migration. However, the nature

of the relationship is diverse and context speciﬁc. For example, displaced

people and migrants may be associated with heightened social tensions

1088

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

in receiving areas through mechanisms such as ecological degradation,

reduced access to services and a disturbed demographic balance in the

host area (Rüegger and Bohnet, 2020). Ghimire etal. (2015) observed

that an inﬂux of ﬂood-displaced people prolonged conﬂict by causing a

lack of access to services for some of the host population and feelings of

grievance. Further, migration from drought-stricken areas to local urban

centres has been used to suggest a climate trigger for the Syrian conﬂict

(e.g., Ash and Obradovich (2020)). However, this link has been strongly

contested by research that contextualises the drought in wider political

economic approaches and existing migration patterns (De Châtel, 2014;

Fröhlich, 2016; Selby, 2019; 16.2.3.9).

There is some evidence of an association between climate-related

rural-to-urban migration and the risk of civil unrest (medium

agreement, low evidence). Petrova (2021) found that while migration

in general was associated with increased protests in urban receiving

areas, the relationship did not hold for hazard-related migration. In

other settings, the association of civil unrest with in-migration was

found to depend on the political alignment of the host state with the

capital (Bhavnani and Lacina, 2015), previous experience of extreme

climate hazards (Koubi et al., 2021) and previous experience of

violence among migrants (Linke etal., 2018). Climate-related migrants

have reported higher levels of perception and experience of violence

in their destination (Linke etal., 2018; Koubi etal., 2018). There has

been no association established between international migration and

conﬂict. The literature highlights how unjust racial logics may generate

spurious links between climate migration and security (Fröhlich, 2016;

Telford, 2018).

7.2.7.4 Gendered Dimensions of Climate-Related Conﬂict

Structural inequalities play out at an individual level to create gendered

experiences of violence (high agreement, medium evidence). Violent

conﬂict is experienced differently by men and women because of

gender norms that already exist in society and shape vulnerabilities.

For example, conﬂict deepens gendered vulnerabilities to climate

change related to unequal access to land and livelihood opportunities

(Chandra et al., 2017). Motivations for inter-group violence may

be inﬂuenced by constructions of masculinity, for example the

responsibility to secure their family’s survival or pay dowries

(Myrttinen etal., 2017), and gendered roles may incentivise young

men to protest or to join non-state armed groups during periods of

adverse climate (Myrttinen etal., 2015; Myrttinen etal., 2017; Anwar

etal., 2019; Hendrix and Haggard, 2015; Koren and Bagozzi, 2017).

Research has found a positive correlation between crop failures and

suicides by male farmers who could not adapt their livelihoods to

rising temperatures (Bryant and Garnham 2015; Kennedy and King,

2014; Carleton, 2017).

Extreme weather and climate impacts are associated with increased

violence against women, girls and vulnerable groups (high agreement,

medium evidence). During and after extreme weather events, women,

girls and LGBTQI people are at increased risk of domestic violence,

harassment, sexual violence and trafﬁcking (Le Masson etal., 2019;

Nguyen, 2019;Myrttinen etal., 2015; Chindarkar, 2012). For example,

early marriage is used as a coping strategy for managing the effects

of extreme weather events (Ahmed et al., 2019) and women are

exposed to increase risk of harassment and sexual assault as scarcity

and gender-based roles cause them to walk longer distances to fetch

water and fuel (Le Masson etal., 2019). Within the household, violent

backlash or heightened tensions may arise from changing gender

norms as men migrate to ﬁnd work in post-disaster settings (Stork

etal., 2015) and men’s use of negative coping mechanisms, such as

alcoholism, when unable to meet norms of providing for the household

(Anwar et al., 2019; Stork et al., 2015). Rates of intimate partner

violence have been found to increase with higher temperatures (Sanz-

Barbero etal., 2018).

7.2.7.5 Observed Impacts on Non-violent Conﬂict and Geopolitics

Climate adaptation and mitigation projects implemented without

taking local interests and dynamics into account have the potential

to cause conﬂict (high agreement, medium evidence). Reforestation

or forest management programmes driven by reducing emissions

through deforestation, land zoning and managed retreat due to sea

level rise have been identiﬁed as having the potential to cause friction

and conﬂict within and between groups and communities (de la Vega-

Leinert etal., 2018; Froese and Schilling, 2019). Conﬂict may arise

when there is resistance to a proposed project, where interventions

favour one group over another, or when projects undermine

livelihoods or displace populations (e.g., Nightingale (2017); Sovacool

et al. (2015); Sovacool (2018); Corbera (2017); Hunsberger (2018);

Sections 4.6.8, 5.13.4, 14.4.7.3). In addition to conﬂict generated

by the poor implementation of land-based climate mitigation and

adaptation projects, Gilmore and Buhaug (2021) highlight the links

between climate policy and conﬂict through the potential effects of

unequal distribution of economic burdens and fossil fuel markets on

economic growth. There is a small literature that draws attention

to the potential security of nuclear proliferation, if nuclear energy

is increasingly employed as a low-carbon energy source (e.g.,

Parthemore etal. (2018); Bunn, (2019)).

Economic and social changes due to changes in sea ice extent in the

Arctic are anticipated to be managed as part of existing governance

structures (high agreement, medium evidence). The opening-up of the

Arctic and associated geopolitical manoeuvring for access to shipping

routes and sub-sea hydrocarbons is often highlighted as a potential

source of climate conﬂict (e.g., Koivurova (2009); Åtland (2013);

Tamnes and Offerdal (2014)). Research assessed in AR5 focused

on the potential for resource wars and Arctic land grabs. However,

research since AR5 is less sensationalist in its approach to Arctic

security, focusing instead on the practicalities of polycentric Arctic

governance under climate change, the economic impacts of climate

change, protecting the human security of Arctic populations whose

autonomy is at risk (Heininen and Exner-Pirot, 2020), understanding

how different regions (e.g., the EU) are positioning themselves more

prominently in the Arctic space (Raspotnik and Østhagen, 2019) and

Arctic Indigenous Peoples’ understanding of security (Hossain, 2016;

Chapter 3; Chapter 14; CCP6).

1089

Health, Wellbeing and the Changing Structure of Communities Chapter 7

7.3 Projected Future Risks under Climate

Change

7.3.1 Projected Future Risks for Health and Well-Being

7.3.1.1 Global Impacts

Climate change is expected to signiﬁcantly increase the health risks

resulting from a range of climate-sensitive diseases and conditions,

with the scale of impacts depending on emissions and adaptation

pathways in coming decades (very high conﬁdence). Sections7.3.1.2

to 7.3.1.11 assess the available studies on future projections for risks

associated with speciﬁc climate-sensitive diseases and conditions

previously described in Section7.2.1. In the case of diabetes, cancer,

injuries, mosquito-borne diseases other than dengue and malaria,

rodent-borne diseases and most mental illnesses, insufﬁcient literature

was found to allow for assessment. Adaptation pathways and options

for managing such risks are detailed in Section7.4.

Even in the absence of further warming beyond current levels, the

proportion of the overall global deaths caused by climate-sensitive

diseases and conditions would increase marginally by mid-century

Projected annual additional deaths attributable to climate change, in 2030 and 2050 compared to 1961–1990



Heat in elderly people  Diarrhoeal disease in children under 15 years Malaria Dengue Undernutrition (stunting)

Total deaths

2030 2050

Australasia &

Oceania

Southern Africa

Central Latin America

& Carribean

North Amercia

South-east Asia

92809

66114

South Asia

Cause of death

6102

2992

7769

4083

Andean

Latin

America

Southern Latin America

Tropical Latin America

Eastern Africa

Dengue

Malaria

Undernutrition

Diarrhoeal

Heat

Asia Pacific, high income

West Africa

Africa and Middle East total

Asia total

Europe total

127418

162383

Australasia &

Oceania total

North America total

Central &

South Amercia total

351

172

Central Asia

East Asia

Central

Africa

Adapted from source: World Health Organization. 2014. Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s.

10473

5485

Europe

North

Africa &

Middle

East

Figure7.8 | Projected additional annual deaths attributable to climate change in 2030 and 2050 compared to 1961–1990 (WHO, 2014).

1090

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

(high conﬁdence). Two global projections of climate change health

impacts have been conducted since AR5. The ﬁrst focused on cause-

speciﬁc mortality for eight exposures for 2030 and 2050 for a mid-

range emissions scenario (A1b) and three scenarios of economic growth

(WHO, 2014). The study estimated that the climate change projected

to occur by 2050 (compared to 1961–1990) could result in an excess

of approximately 250,000 deaths yr

–1

, dominated by increases in

deaths due to heat (94,000, mainly in Asia and high-income countries),

childhood undernutrition (85,000, mainly in Africa but also in Asia),

malaria (33,000, mainly in Africa) and diarrhoeal disease (33,000, mainly

in Africa and Asia). Overall, more than half of this excess mortality is

projected for Africa. Near-term projections (for 2030) are predominantly

for childhood undernutrition (95,200 out of 241,000 total excess deaths)

(Figure 7.8). The second study (Carleton et al. 2020) focused on all-

cause mortality associated with warming under both a high emissions

scenario (RCP8.5) and a middle emissions scenario (RCP4.5). The

analyses created a metric of death equivalents that accounted for hot

and cold temperature-related mortality and the costs of individual level

adaptation; no acclimatization or community-level adaptation, such as

early warning systems, were incorporated. Average annual temperature-

mortality-income per capita relationships estimated from pooled data

from 40 predominantly middle- and high-income countries (38% of the

world population) were applied worldwide. Under the high emissions

scenario, climate change was projected to result in approximately 85

deaths equivalents per 100,000 population.

Temperature increases are projected to exceed critical risk thresholds

for six key climate-sensitive health outcomes, highlighting the criticality

of building adaptive capacity in health systems and in other sectors

that inﬂuence health and well-being (high conﬁdence). Recently

reported research illustrates the temperature thresholds underthree

adaptation scenarios describing the effectiveness of health systems

to manage additional risks from climate change for heat-related

morbidity and mortality; ozone-related mortality; malaria incidence

rates; incidence rates of Dengue and other diseases spread byAedes

sp. mosquitos; Lyme disease; and West Nile fever(Ebi etal., 2021a). As

shown in Figure7.9,these adaptation scenarios signiﬁcantly alterthe

warming thresholds at which risks accelerate, with the proactive

adaptation scenario, a scenario that emphasises international

cooperation towards achieving sustainable development, having

the greatest potential to avoid signiﬁcant increases in risks under

all but the highest levels of warming. The incomplete adaptation

scenariodescribes a world with moderate challenges to adaptation

and mitigation.The limited adaptation scenariodescribes a world with

high challenges to adaptation and mitigation. In the ﬁgure, transitions

are based on the peer-reviewed literature projecting risks for each of

the health outcomes. Projections for time intervals were changed to

temperature increase above pre-industriallevels based on the climate

models and scenarios used in the projections.The assessed projections

were based on a range of scenarios,including SRES, CMIP5, and ISIMIP,

and, in some cases, demographic trends.The black dots are levels of

Climate sensitive health outcomes under three adaptation scenarios

Scenario narratives

Limited adaptation: Failure to proactively adapt; low investment in health systems.

Incomplete adaptation: Incomplete adaptation planning; moderate investment in health systems.

Proactive adaptation: Proactive adaptive management; higher investment in health systems

Historical average

temperature increase

in 2011–2020 was

1.09°C (dashed line)

range 0.95–1.20°C

Confidence level

assigned to

transition

range

Risk/impact

Low Very high

Very high

High

Moderate

Undetectable

•

•••

••

••••

Transition range

* Mortality projections

include demographic

trends but do not

include future efforts

to improve air quality

that reduce ozone

concentrations.

1.5

Global surface temperature change (°C)

Limited

adaptation

Heat-related morbidity

and mortality

Proactive

adaptation

Incomplete

adaptation

•••••••••

••••

•••••••

Limited

adaptation

Ozone-related mortality *

Proactive

adaptation

Incomplete

adaptation

••••••••

••••

•••••••

Limited

adaptation

Malaria

Incomplete

adaptation

Proactive

adaptation

•••••••

••••••

••••

1.5

Global surface temperature change (°C)

•••• ••• ••

•••• •••

••••

Lyme disease

Limited

adaptation

Incomplete

adaptation

Proactive

adaptation

••• ••

•

••• ••

•••

West Nile fever

Limited

adaptation

Incomplete

adaptation

Proactive

adaptation

Limited

adaptation

Dengue and other diseases carried

by species of Aedes mosquitoes

Proactive

adaptation

Incomplete

adaptation

•••••••••

••••

•••••••

Figure7.9 | Climate-sensitive human health outcomes under three adaptation scenarios.

1091

Health, Wellbeing and the Changing Structure of Communities Chapter 7

conﬁdence, from very high (four dots) to low (one dot).The diagrams

for the proactive and incomplete adaptation scenarios are truncated at

the nearest whole °C within the range of temperature change in 2100

under three SSP scenarios used in panel (a) of SPM.3.

7.3.1.2 Projected Changes in Heat- and Cold-Related Exposure

and Related Health Outcomes

This section considers the broad impacts of projected changes in heat-

and cold-related exposure and related outcomes including mortality

and work productivity. Several of the most common heat- and cold-

related speciﬁc health outcomes (e.g., CVD) are assessed individually

in later sections of this chapter.

Population heat exposure will increase under climate change

(very high conﬁdence). Since AR5 there has been considerable

progress with quantifying future human exposure to extreme heat

(Schwingshackl etal., 2021), especially as determined by different

combinations of SSPs and RCPs (Chambers, 2020; Cheng et al.,

2020; Jones etal., 2018; Liu etal., 2017; Ma and Yuan, 2021; Russo

etal., 2019). For example, Table7.1 shows projections of population

exposure to heatwaves, as expressed by the number of person-days,

for the 2061–2080 period aggregated by geographical region and

SSP/RCP. At the global level, projected future exposure increases

from approximately 15million person-days for the current period to

535billion person-days for high population growth under the high

GHG emission SSP3-RCP8.5 scenario, while for the low population

growth/high urbanisation and business as usual SSP5-RCP4.5

scenario, the exposure is substantially lower at 170billion person-

days. Spatial variations in future heatwave frequency and population

growth play out in the form of signiﬁcant geographical contrasts in

exposure, with the largest increases projected for low latitude regions

such as India and signiﬁcant portions of sub-Saharan Africa, where

increases in heatwave frequency and population are expected. Over

East Asia and especially eastern China, exposures are projected to

rise, with the effect of increases in heatwave frequency exceeding

the countering effect of projected reductions in population, especially

in non-urban areas. Further, for North America and Europe, where

rural depopulation is projected, the predominant driver of increases

in exposure is urban growth (Jones etal., 2018).

Comparisons of heatwave exposure for 1.5°C and 2.0°C warming

for different SSPs indicate strong geographical contrasts in potential

heatwave risk (high conﬁdence). One global level assessment for a

1.5°C warming projects that low human development index countries

will experience exposure levels equal to or greater than the exposure

levels for very high human development index countries under a 2°C

warming (Russo, 2019). The same assessment also ﬁnds that holding

global warming below 1.5°C in tandem with achieving sustainable

socioeconomic development (e.g., SSP1 as opposed to SSP4) yields

reduced levels of heatwave exposure, especially for low human

development index countries, particularly across sub-Saharan Africa.

Similar ﬁndings were found in other global level assessments. Global

exposure to extreme heat increases almost 30times under a SSP3-

8.5 combination, with the average exposure for Africa 118 times

greater than historical levels, in stark contrast to the four-fold increase

projected for Europe. Compared to a SSP3-8.5 scenario, exposure was

reduced by 65% and 85% under the SSP2-4.5 and SSP1-2.6 scenarios,

respectively (Liu etal., 2017).

Regional level assessments of changes in population heat exposure for

Africa, Europe, the USA, China and India corroborate general ﬁndings

at the global level, that the impact of warming is ampliﬁed under

divergent regional development pathways (e.g., SSP4 – inequality)

compared to those fostering sustainable development (e.g., SSP1 –

sustainability) (high conﬁdence) (Rohat et al., 2019a; Weber et al.,

2020; Broadbent etal., 2020; Dahl etal., 2019; Harrington and Otto,

2018; Rohat etal., 2019b; Vahmani etal., 2019; Huang and etal., 2018;

Zhang etal., 2020a; Liu etal., 2017). For some regions, such as Europe,

changes in exposure are projected to be largely a consequence of

climate change, while for others, such as Africa and to a lesser extent

Asia, Oceania, North America and South America, the interactive effects

of demographic and climate change are projected to be important

(Jones etal., 2018; Liu etal., 2017; Russo etal., 2016; Ma and Yuan,

2021) (medium conﬁdence).

Compared to research that estimates the temperature only impacts of

climate change on heat-related mortality (see below), the number of

studies that explicitly model mortality responses considering various

combinations of SSPs and RCPs is small and mostly restricted to the

country or regional level. These studies point to increases in heat-

Table7.1 | Projected exposure to heatwaves in millions of person-days by region under different SSP/RCP combinations.

Region

Exposure in millions of person-days

Current SSP3-4.5 SSP5-4.5 SSP3-8.5 SSP5-8.5

Global

USA

North America

Europe

Latin America and

Caribbean

North Africa and Middle

East

Sub-Saharan Africa

Russia and Central Asia

South Asia

East Asia

Southeast Asia

Oceania

14,811

375

376

191

803

1,335

1,427

272

7,194

977

711

244,807

4,769

4,821

2,967

17,287

34,721

67,442

3,074

84,044

12,176

12, 452

247

168,488

8,671

8,778

3,775

10,856

23,160

41,339

1,951

53,655

10,855

9,146

492

534,848

10,802

10,990

7,326

45,612

65,072

158,290

6,554

146,709

35,381

60,909

822

374,269

19,646

20,153

9,969

28,435

43,648

96,054

4,360

94,288

31,918

47,141

1,158

1092

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

related mortality especially amongst the elderly across a range of SSPs,

with the greatest increases under SSP5 and RCP8.5 (Rail etal., 2019;

Yang etal., 2021).

Estimates of heat-related mortality based solely on changes in

temperature point to elevated levels of global and regional level

mortality compared to the present, with the magnitude of this

increasing from RCP4.5 through to RCP8.5 (high conﬁdence)

(Ahmadalipour and Moradkhani, 2018; Cheng etal., 2019; Kendrovski

etal., 2017; Lee etal., 2020; Limaye etal., 2018; Moreﬁeld etal., 2018).

Further support comes from the projection that heat-related health

impacts for a 2°C increase in global temperatures will be greater than

those for 1.5°C warming (very high conﬁdence) (Dosio etal., 2018;

Mitchell etal., 2018; King and Karoly, 2017; Vicedo-Cabrera etal.,

2018a).

Estimates of future mortality that incorporate adaptation in addition to

temperature change point to increases in heat-related mortality under

global warming, albeit at lower levels than the case of no adaptation

(high conﬁdence) (Anderson et al., 2018; Gosling et al., 2017; Guo

etal., 2018; Honda and Onozuka, 2020; Vicedo-Cabrera etal., 2018b;

Wang et al., 2018b). Whether adaptation is considered or not, the

consensus is Central and South America, southern Europe, southern

and Southeast Asia and Africa will be the most affected by climate

change in terms of heat-related mortality (high conﬁdence). Similarly,

projections of the impacts of future heat on occupational health, worker

productivity and workability point to these regions as problematic

under climate change (high conﬁdence) (Andrews etal., 2018; de Lima

etal., 2021; Dillender, 2021; Kjellstrom etal., 2018; Orlov etal., 2020;

Rao etal., 2020; Tigchelaar etal., 2020), especially for occupations

with high exposure to heat, such as agriculture and construction. This

accords with the ﬁndings from independent projections of population

heat exposure as outlined above (high conﬁdence).

The effect of climate change on productivity is projected to reduce

GDP at a range of geographical scales (high conﬁdence) (Borg etal.,

2021; Oppermann et al., 2021; Orlov et al., 2020). For example,

measuring economic costs using occupational health and safety

recommendations, it was estimated that RCP8.5 would result in a

2.4% reduction in global GDP compared to a 0.5% reduction under

RCP2.6 (Orlov etal., 2020). For the USA, it was estimated that the

total hours of labour supplied declined ∼0.11% (±0.004%) per degree

Celsius increase in global mean surface temperature for low-risk

workers and 0.53% (±0.01%) per degree Celsius increase for high-

risk workers exposed to outdoor temperatures (Hsiang etal., 2017).

Further, a systematic review of the literature indicates that extreme

heat exacts a substantial economic burden on health systems, which

bears implications for future heat-attributable healthcare costs

(Wondmagegn etal., 2019).

Since AR5, there has been an increase in the understanding of the extent

to which a warming world is likely to affect cold- or winter-related

health impacts. Future increases in heat-related deaths are expected

to outweigh those related to cold (high conﬁdence) (Aboubakri etal.,

2020; Achebak etal., 2020; Burkart etal., 2021; Huber etal., 2020b;

Martinez etal., 2018; Rodrigues etal., 2020; Vardoulakis etal., 2014;

Weinberger etal., 2017; Weinberger etal., 2018a; Weitensfelder and

Moshammer, 2020). However, strong regional contrasts in heat- and

cold-related mortality trends are likely under a RCP8.5 scenario, with

countries in the Global North experiencing minimal to moderate

decreases in cold-related mortality while warm climate countries in the

Global South are projected to experience increases in heat-attributable

deaths by the end of the century (Gasparrini etal., 2017; Burkart etal.,

2021). Projections of the magnitude of change in the temperature-

related burden of disease do, however, demonstrate great variability,

due to the application of a wide range of climate change, adaptation

and demographic scenarios (Cheng etal., 2019).

A particular focus since AR5 has been the impact of climate change

on cities (see AR6 Chapter 6). Heat risks are expected to be greater

in urban areas due to changes in regional heat exacerbated by ‘heat

island’ effects (high conﬁdence) (Doan and Kusaka, 2018; Heaviside

etal., 2016; Li etal., 2021; Rohat etal., 2019a; Rohat etal., 2019c;

Varquez et al., 2020; Wouters et al., 2017; Zhao et al., 2021), with

intra-urban scale variations in heat exposure attributable to land cover

contrasts and urban form and function (Avashia et al., 2021; Jang

et al., 2020; Macintyre et al., 2018; Schinasi etal., 2018). However,

further research is required to establish the health implications of

increasing chronic slow-onset extreme heat (Oppermann etal., 2021)

in addition to the acute health outcomes of UHI–heatwave synergies

under climate change. The latter is particularly important as studies

that address UHI–heatwave interactions have mainly focused on

changes in UHI intensity (e.g., Ramamurthy and Bou-Zeid (2017); Scott

etal. (2018)). Whether signiﬁcant urban mortality anomalies arise from

the interplay of heatwaves and UHIs largely remains an open question

although at least one study demonstrated higher urban compared

to rural mortality rates during heatwaves (Ruuhela etal., 2021). The

beneﬁts of the winter UHI effect for cold-related mortality remain

largely unexplored, but one study for Birmingham, UK, indicates the

winter UHI will continue to have a protective effect in future climate

(Macintyre etal., 2021).

7.3.1.3 Projected Impacts on Vector-Borne Diseases

The distribution and abundance of disease vectors, and the transmission

of the infections that they carry, are inﬂuenced both by changes in

climate and by trends such as human population growth and migration,

urbanisation, land use change, biodiversity loss and public health

measures. Each of these may increase or decrease risk, interact with

climate effects and may contribute to the emergence of infectious

disease, although there are few studies assessing future risk of

emergence (Gibb etal., 2020). Unless stated otherwise, the assessments

below are speciﬁcally for the effects of climate change on individual

diseases, assuming other determinants remain constant.

There is a high likelihood that climate change will contribute to increased

distributional range and vectorial capacity of malaria vectors in parts

of sub-Saharan Africa, Asia and South America (high conﬁdence).

In Nigeria, the range and abundance of Anopheles mosquitoes are

projected to increase under both lower (RCP2.6) and especially under

higher emissions scenarios (RCP8.5) due to increasing and ﬂuctuating

temperature, longer tropical rainfall seasons and rapid land use

changes (Akpan etal., 2018). Similarly, vegetation acclimation due to

elevated atmospheric CO

under climate change will likely increase the

1093

Health, Wellbeing and the Changing Structure of Communities Chapter 7

abundance of Anopheles vectors in Kenya (Le etal., 2019). Distribution

of Anopheles may decrease in parts of India and Southeast Asia, but

there is an expected increase in vectorial capacity in China (Khormi and

Kumar, 2016). In South America, climate change is projected to expand

the distributions of malaria vectors to 35–46% of the continent by 2070,

particularly species of the Albitarsis complex (Laporta etal., 2015).

Malaria infections have signiﬁcant potential to increase in parts

of sub-Saharan Africa and Asia, with risk varying according to the

warming scenario (medium conﬁdence). In Africa, where most

malaria is due to the more deadly Plasmodium falciparum parasite,

climate change is likely to increase the overall transmission risk due

to the likely expansion of vector distribution and increase in biting

rates (Bouma etal., 2016; M’Bra etal., 2018; Nkumama etal., 2017;

Ryan et al., 2015b; Tompkins and Caporaso, 2016a). The projected

effect of climate change varies markedly by region, with projections

for west Africa tending to indicate a shortening of transmission

seasons and neutral or small net reductions in overall risk, whereas

studies consistently project increases in southern and eastern Africa,

with potentially an additional 76million people at risk of endemic

exposure (10–12months yr

–1

) by the 2080s (Nkumama etal., 2017;

Ryan etal., 2015b; Semakula etal., 2017; Zaitchik, 2019; Leedale etal.,

2016; Murdock etal., 2016; Yamana etal., 2016; Ryan etal., 2020). In

sub-Saharan Africa, malaria case incidence associated with dams in

malaria-endemic regions will likely be exacerbated by climate change,

with signiﬁcantly higher rates projected under RCP8.5 in comparison

Projected change in the abundance of Aedes aegypti

<-200 -100 0 >200

Potential abundance change (2090–2099) - (1987–2016)

100

(a) RCP2.6

(b) RCP8.5

Figure7.10 | Projected change in the potential abundance of Aedes aegypti over the 21st century (2090–2099 relative to 1987–2016) (Liu-Helmersson

etal., 2019).

1094

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

to lower-emission scenarios (Kibret etal., 2016). Incidence of malaria

in Madagascar is projected to increase under RCP4.5 through RCP8.5

(Rakotoarison etal., 2018). Distribution of P. vivax and P. falciparum

malaria in China is likely to increase under RCPs higher than 2.6,

especially RCP8.5 (Hundessa etal., 2018). In India, projected scenarios

for the 2030s under RCP4.5 indicate changes in the spatial distribution

of malaria, with new foci and potential outbreaks in the Himalayan

region, southern and eastern states, and an overall increase in months

suitable for transmission overall, with some other areas experiencing a

reduction in transmission months (Sarkar etal., 2019).

Rising temperatures are likely to cause poleward shifts and overall

expansion in the distribution of mosquitoes Aedes aegypti and Aedes

albopictus, the principal vectors of dengue, yellow fever, chikungunya

and Zika (high conﬁdence). Globally, the population exposed to

disease transmission by one of these vectors is expected to increase

signiﬁcantly due to the combination of climate change and non-climatic

processes including urbanisation and socioeconomic inter-connectivity,

with exposure rates rising under higher warming scenarios (Kamal

etal., 2018; Kraemer etal., 2019). For example, approximately 50% of

the global population is projected to be exposed to these vectors by

2050 under RCP6.0 (Kraemer etal., 2019). The effect of climate change

alone is projected to increase the population exposed to Aedes aegypti

by 8–12% by 2061–2080 (Monaghan etal., 2018), and its abundance

is projected to increase by 20% under RCP2.6 and 30% under RCP8.5

by the end of the century (Liu-Helmersson etal., 2019; Figure7.10).

Exposure to transmission by Aedes albopictus speciﬁcally would be

highest at intermediate climate change scenarios and would decrease

in the warmest scenarios (Ryan etal., 2019). Under scenarios other

than RCP2.6, most of Europe would experience signiﬁcant increases in

exposure to viruses transmitted by both vectors (Liu-Helmersson etal.,

2019).

Climate change is expected to increase dengue risk and facilitate

its global spread, with the risk being greatest under high emissions

scenarios (high conﬁdence). Future exposure to risk will be inﬂuenced

by the combined effects of climate change and non-climatic factors

such as population density and economic development (Akter etal.,

2017). Overall, risk levels are expected to rise on all continents (Akter

et al., 2017; Messina et al., 2015; Rogers, 2015; Liu-Helmersson

etal., 2016; Messina etal., 2019). Compared to 2015, an additional

1 billion people are projected to be at risk of dengue exposure by

2080 under an SSP1-4.5 scenario, 2.25billion under SSP2-6.0, and

5billion under SSP3-8.5 (Messina etal., 2019). In North America, risk

is projected to expand in north-central Mexico, with annual dengue

incidence in Mexico increasing by up to 40% by 2080, and expand

from US southern states to mid-western regions (Proestos etal., 2015;

Colon-Gonzalez etal., 2013). In China, under RCP8.5, dengue exposure

would increase from 168million people in 142 counties to 490million

people in 456 counties by the late 2100s (Fan and Liu, 2019). In Nepal,

dengue fever is expected to expand throughout the 2050s and 2070s

under all RCPs (Acharya etal., 2018). In Tanzania, there is a projected

shift in distribution towards central and northeastern areas and risk

intensiﬁcation in nearly all parts of the country by 2050 (Mweya etal.,

2016). Dengue vectorial capacity is projected to increase in Korea

under higher RCP scenarios (Lee etal., 2018a).

There are insufﬁcient studies for assessment of projected effects of

climate change on other arboviral diseases, such as chikungunya

and Zika. Zika virus transmits under different temperature optimums

than does dengue, suggesting environmental suitability for Zika

transmission could expand with future warming (low conﬁdence)

(Tesla etal., 2018).

Climate change can be expected to continue to contribute to the

geographical spread of the Lyme disease vector Ixodes scapularis

(high conﬁdence) and the spread of tick-borne encephalitis and

Lyme disease vector Ixodes ricinus in Europe (medium conﬁdence).

In Canada, vector surveillance of the black-legged tick I. scapularis

identiﬁed strong temperature effects on the limits of their occurrence,

on recent geographic spread, temporal coincidence in emergence of

tick populations and acceleration of the speed of spread (Clow etal.,

2017; Cheng etal., 2017). In Europe, increasing temperatures over the

1950–2018 period signiﬁcantly accelerated the life cycle of Ixodes

ricinus and contributed to its spread (Estrada-Peña and Fernández-

Ruiz, 2020). Under RCP4.5 and RCP8.5 scenarios, projections indicate a

northward and eastward shift of the distribution of I. persulcatus and I.

ricinus, vectors of Lyme disease and tick-borne encephalitis in northern

Europe and Russia, with an overall large increase in distribution in

the second half of the current century (Popov and Yasyukevich, 2014;

Yasjukevich et al., 2018) and increases in intensity of tick-borne

encephalitis transmission in central Europe (Nah etal., 2020).

Climate change is projected to increase the incidence of Lyme

disease and tick-borne encephalitis in the Northern Hemisphere (high

conﬁdence) (Figure 7.9). The basic reproduction number (R0) of I.

scapularis in at least some regions of Canada is projected to increase

under all RCP scenarios (McPherson etal., 2017). In the USA, a 2°C

warming could increase the number of Lyme disease cases by over

20% over the coming decades and lead to an earlier onset and longer

length of the annual Lyme disease season (Dumic and Severnini, 2018;

Monaghan etal., 2015).

Climate change is projected to change the distribution of schistosomiasis

in Africa and Asia (high conﬁdence), with a possible increase in global

land area suitable for transmission (medium conﬁdence). A global

increase in land area with temperatures suitable for transmission

by the three main species of Schistosoma (S. japonicum, S. mansoni

and S. haematobium) is projected under the RCP4.5 scenario for the

2021–2050 and 2071–2100 periods (Yang and Bergquist, 2018), but

regional outcomes are expected to vary. In Africa, shifting temperature

regimes associated with climate change are expected to lead to

reduced snail populations in areas with already high temperatures and

higher populations in areas with currently low winter temperatures

(Kalinda etal., 2017; McCreesh and Booth, 2014). Infection risk with

Schistosoma mansoni may increase by up to 20% over most of eastern

Africa over the next 20–50years but decrease by more than 50% in

parts of north and east Kenya, southern South Sudan and eastern

People’s Democratic Republic of Congo (PDRC) (McCreesh et al.,

2015), with a possible overall net contraction (Stensgaard etal., 2013).

In China, currently endemic areas in Sichuan Province may become

unsuitable for snail habitats, but currently non-endemic areas in

Sichuan and Hunan/Hubei provinces may see a new emergence (Yang

and Bergquist, 2018). In addition to the projected effects of temperature

1095

Health, Wellbeing and the Changing Structure of Communities Chapter 7

described above, distribution and transmission of schistosomiasis will

also be affected positively or negatively by changes in the availability

of freshwater bodies, which were not included in these models.

7.3.1.4 Projected Impacts on Waterborne Diseases

Climate change will contribute to additional deaths and mortality

due to diarrhoeal diseases in the absence of adaptation (medium

conﬁdence) (see Figure 7.8). Risk factors for future excess deaths

due to diarrhoeal diseases are highly mediated by future levels of

socioeconomic development and adaptation. An additional 1°C

increase in mean average temperature is expected to result in a 7%

(95% CI, 3–10%) increase in all-cause diarrhoea (Carlton etal., 2016),

an 8% (95% CI, 5–11%) increase in the incidence of diarrheic E. coli

(Philipsborn etal., 2016) and a 3–11% increase in deaths attributable

to diarrhoea (WHO, 2014). WHO Quantitative Risk Assessments for the

effects of climate change on selected causes of death for the 2030s

and 2050s project that overall deaths from diarrhoea should fall due

to socioeconomic development but that the effect of climate change

under higher emission scenarios could cause an additional 48,000

deaths in children aged under 15years in 2030 and 33,000 deaths

for 2050, particularly in Africa and parts of Asia. In Ecuador, projected

increases in rainfall variability and heavy rainfall events may increase

diarrhoea burden in urban regions (Deshpande etal., 2020). A limit in

the assessable literature is a lack of studies in the highest risk areas

(Liang and Gong, 2017; UNEP, 2018).

Climate change is expected to increase future health risks associated

with a range of other WBDs and parasites, with effects varying by

region (medium conﬁdence). WBDs attributable to protozoan parasites

including Cryptosporidium spp. and Giardia duodenalis (intestinalis)

are expected to increase in Africa due to increasing temperatures

and drought (Ahmed etal., 2018; Efstratiou etal., 2017). Recent data

suggest a poleward expansion of Vibrios to areas with no previous

incidence, particularly in mid- to high-latitude regions in areas where

rapid warming is taking place (Baker-Austin etal., 2017). The number

of Vibrio-induced diarrhoea cases yr

–1

increased in past decades in the

Baltic Sea region, and the projected risk of vibriosis will increase in

northern areas, where waters are expected to become warmer and

more saline due to reduced precipitation and have higher chlorophyll

concentrations (Escobar etal., 2015; Semenza etal., 2017).

The risk of Campylobacteriosis and other enteric pathogens could rise

in regions where heavy precipitation events or ﬂooding are projected to

increase (medium conﬁdence). In Europe, the risk of Campylobacteriosis

and diseases caused by other enteric pathogens could rise in regions

where precipitation or extreme ﬂooding are projected to increase

(European Environment Agency, 2017), although incidence rates may

be further mediated by seasonal social activities (Rushton etal., 2019;

Williams et al., 2015b). Accelerated releases of dissolved organic

matter to inland and coastal waters through increases in precipitation

are expected to reduce the potential for solar ultraviolet inactivation of

pathogens and increase risks for associated WBDs (Williamson etal.,

2017). The combined relative risk for waterborne campylobacteriosis,

salmonellosis and diseases due to Verotoxin-producing Escherichia coli

was estimated to be 1.1 (i.e., a 10% increase) for every 1°C in mean

annual temperature, while by the 2080s, under RCP8.5, annual rates of

cryptosporidiosis and giardiasis could rise by approximately 16% due

to more severe precipitation events (Brubacher etal., 2020; Chhetri

etal., 2019).

7.3.1.5 Projected Impacts on Food-Borne Diseases

The prevalence of Salmonella infections is expected to rise as higher

temperatures enable more rapid replication (medium conﬁdence).

Research from Canada ﬁnds a very strong association of salmonellosis

and other FBDs with higher temperatures, suggesting that climate

change could increase food safety risks ranging from increased public

health burden to emergent risks not currently seen in the food chain

(Smith and Fazil, 2019). In Europe, the average annual number of

temperature-related cases of salmonellosis under high emissions

scenarios could increase by up to 50% more than would be expected on

the basis of on population change alone by 2100 (Lake, 2017; European

Environment Agency, 2017). Warming trends in the southern USA may

lead to increased rates of Salmonella infections (Akil etal., 2014).

7.3.1.6 Projected Impacts on Pollution- and Aeroallergens-

Related Health Outcomes

Global air pollution-related mortality attributable directly to climate

change—the human health climate penalty associated with climate-

induced changes in air quality—is likely to increase and partially

counteract any decreases in air pollution-related mortality achieved

through ambitious emission reduction scenarios or stabilisation of

global temperature change at 2°C (medium conﬁdence). Demographic

trends in aging and more vulnerable population are likely to be

important determinants of future air quality—a human health climate

penalty (high conﬁdence).

Poor air quality contributes to a range of NCDs, including cardiovascular,

respiratory and neurological, commonly resulting in hospitalisation or

death. This section considers the possible risks for health of future

climate-related changes in ozone and PM. The climate penalty, the

degree to which global warming could affect future air quality, is better

understood for ozone than for PM (von Schneidemesser etal., 2020).

This is because increases in air temperature enhance ozone formation

via associated photochemical processes (Archibald etal., 2020; Fu and

Tian, 2019). The association between climate and PM is complex and

moderated by a diverse range of PM components as well as formation

and removal mechanisms (von Schneidemesser etal., 2020), added

to which is uncertainty about future climate-related PM sources such

as wildﬁres (Ford etal., 2018) and changes in aridity (Achakulwisut

etal., 2019). As noted in AR6 WGI Chapter 6 (Naik et al 2021), future

air quality will largely depend on precursor emissions, with climate

change projected to have mixed effects. Because of the uncertainty

in how natural processes will respond, there is low conﬁdence in the

projections of surface ozone and PM under climate change (Naik

etal., 2021). This has implications on the levels of conﬁdence in the

projections of the health climate penalty associated with climate-

induced changes in air quality (Orru etal., 2017; Orru etal., 2019; Silva

etal., 2017).

There is a rich literature on global and regional level projections of

air quality-related health effects arising from changes in emissions.

1096

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Comparatively few studies assess how changes in air pollution

directly attributable to climate change are likely to affect future

mortality levels. Projections indicate that emission reduction scenarios

consistent with stabilisation of global temperature change at 2°C or

below would yield substantial co-beneﬁts for air quality-related health

outcomes (Chowdhury etal., 2018b; von Schneidemesser etal., 2020;

Silva etal., 2016c; Markandya etal., 2018; Orru etal., 2019; Shindell

etal., 2018) (high conﬁdence). For example, by 2030, compared to

2000, it was estimated that globally and annually 289,000 PM2.5-

related premature deaths could have been avoided under RCP4.5

compared to 17,200 PM2.5-related excess premature deaths under

RCP8.5 (Silva etal., 2016c). Further, and notwithstanding estimated

reductions in global PM2.5 levels and an associated increase in the

number of avoidable deaths, the beneﬁts of following a low emissions

pathway are expected to be apparent by 2100, with avoidable deaths

estimated at 2.39million deaths yr

–1

under RCP4.5. This contrasts with

the 1.31million deaths estimated under RCP8.5. A few projections

of the health-related climate penalty indicate a possible increase in

ozone and PM2.5-associated mortality under RCP8.5 (Doherty etal.,

2017; Orru etal., 2019; Silva etal., 2017).

At the global level for PM2.5, annual premature deaths due to

climate change were projected to be 55,600 (−34,300 to 164,000)

and 215,000 (−76,100 to 595,000) in 2030 and 2100, respectively,

countering by 16% the projected decline in PM2.5-related mortality

between 2000 and 2100 without climate change (Silva etal., 2017).

Similarly for ozone, the number of annual premature ozone-related

deaths due to climate change was projected to be 3,340 in 2030 and

43,600 in 2050, with climate change accounting for 1.2% (14%) of

the annual premature deaths in 2030 (2100) (Silva etal., 2017). These

global level projections average over considerable geographical

variations (Silva etal., 2017). Projections of the climate change effect

on ozone mortality in 2100 were greatest for East Asia (41 deaths

–1

per million people), India (8 deaths yr

–1

per million people)

and North America (13 deaths yr

–1

per million people). For PM2.5,

mortality was projected to increase across all regions except Africa

(−25,200 deaths yr

–1

per million people) by 2100, with estimated

increases greatest for India (40 deaths yr

–1

per million people), the

Middle East (45 deaths yr

–1

per million people), East Asia (43 deaths

–1

per million people) and the Former Soviet Union (57 deaths yr

–1

per million people). Overall, higher ozone-related health burdens

were projected to occur in highly populated regions, and greater

PM2.5 health burdens were projected in high PM emission regions

(Doherty etal., 2017).

For central and southern Europe, climate change alone could result

in an 11% increase in ozone-associated mortality by 2050. However,

projected declines in ozone precursor emissions could reduce the

EU-wide climate change effect on ozone-related mortality by up to

30%; the reduction was projected to be approximately 24% if aging

and an increasingly susceptible population were accounted for in

projections to 2050 (Orru etal., 2019). For the USA in 2069, the impact

of climate change alone on annual PM2.5- and ozone-related deaths

was estimated to be 13,000 and 3,000 deaths, respectively, with heat-

driven adaptation of air conditioning accounting for 645 and 315

of the PM2.5- and ozone-related annual excess deaths, respectively

(Abel etal., 2018). An aging population is a determinant of future air

quality-related mortality levels. An aging population along with an

increase in the number of vulnerable people may work to offset the

decrease in deaths associated with a low emission pathway (RCP4.5)

and possibly dominate the net increase in deaths under a business as

usual pathway (RCP8.5) (Chen etal., 2020; Doherty etal., 2017; Hong

etal., 2019; Schucht etal., 2015).

Complementing the longer-term changes in air quality arising from

climate change are those associated with air pollution sensitive short-

term meteorological events, such as heatwaves. Studies of individual

heat events (Garrido-Perez etal., 2019; Johansson etal., 2020; Kalisa

etal., 2018; Pu etal., 2017; Pyrgou etal., 2018; Schnell and Prather,

2017; Varotsos etal., 2019) and systematic reviews (Anenberg etal.,

2020) provide evidence for synergistic effects of heat and air pollution.

However, the health consequences of a possible additive effect of air

pollutants during heatwave events were heterogeneous, varying by

location and moderated by socioeconomic factors at the intra-urban

scale (Analitis etal., 2014; Fenech etal., 2019; Krug etal., 2020; Pascal

etal., 2021; Schwarz etal., 2021; Scortichini etal., 2018). This, combined

with the challenges associated with projecting future concentrations

of health-relevant pollutants during heatwave events (Jahn and Hertig,

2021; Meehl etal., 2018), makes it difﬁcult to say with any certainty

that synergistic effects of heat and poor air quality will result in a

heatwave–air pollution health penalty under climate change.

The burden of disease associated with aeroallergens is anticipated to

grow due to climate change (high conﬁdence). The incidence of pollen

allergy and associated allergic disease increases with pollen exposure,

and the timing of the pollen season and pollen concentrations are

expected to change under climate change (Beggs, 2021; Ziska etal.,

2019; Ziska, 2020). The overall length of the pollen season and total

seasonal pollen counts/concentrations for allergenic species such as

birch (Betula) and ragweed (Ambrosia) are expected to increase as a

result of CO

fertilisation and warming, leading to greater sensitisation

(Hamaoui-Laguel etal., 2015; Lake etal., 2017; Zhang etal., 2013).

Changes in pollen levels for several species of trees and grasses are

projected to increase annual emergency department visits in the USA

by between 8% for RCP4.5 and 14% for RCP8.5 by the year 2090

(Neumann et al., 2019) with the exposure to some pollen types

estimated to double beyond present levels in Europe by 2041–2060

(Lake etal., 2017). The prospect of increases in summer thunderstorm

events under climate change (Brooks, 2013) may hold implications

for changes in the occurrence of epidemic thunderstorm asthma

(Bannister et al., 2021; Emmerson et al., 2021; Price et al., 2021).

Similarly, projected alterations in hydroclimate under climate change

may bear implications for increased exposure to mould allergens in

some climates (D’Amato etal., 2020; Paudel etal., 2021).

7.3.1.7 Future Risks Related to Cardiovascular Diseases

Climate change is expected to increase heat-related CVD mortality

by the end of the 21st century, particularly under higher emission

scenarios (high conﬁdence). Most modelling studies conducted since

AR5 project higher rates of heat-related CVD mortality throughout

the remainder of this century (Huang and etal., 2018; Li etal., 2015;

Li et al., 2018; Limaye et al., 2018; Zhang et al., 2018a; Silveira

et al., 2021a; Yang et al., 2021). CVD mortality in Beijing, China,

1097

Health, Wellbeing and the Changing Structure of Communities Chapter 7

could increase by an average of 18.4%, 47.8% and 69.0% in the

2020s, 2050s and 2080s, respectively, under RCP4.5 and by 16.6%,

73.8% and 134%, respectively, under RCP8.5 relative to a 1980s

baseline (Li etal., 2015). Projections of temperature-related mortality

from CVD for Beijing in the 2080s vary depending on RCP and

population assumptions (Zhang etal., 2018a). Projections for Ningo,

China, suggest heat-related years of life lost (YLL) could increase

signiﬁcantly in the month of August by between 3 and 11.5times

over current baselines by the 2070s, even with adaptation (Huang

and et al., 2018). Yang and colleagues project that heat-related

excess CVD mortality in China could increase to approximately 6%

(from a 2010 baseline of under 2%) by the end of the century under

RCP8.5 and to over 3% under RCP4.5 (Yang etal., 2021). The future

burden of temperature-related myocardial infarctions in Germany is

projected to rise under high emissions scenarios (Chen etal., 2019),

while in the eastern USA, Limaye etal. (2018) projected an additional

11,562 annual deaths (95% CI: 2,641–20,095) by mid-century due to

cardiovascular stress in the population 65years of age and above.

CVD mortality in Brazil is projected to increase up to 8.6% by the end

of the century under RCP8.5, compared with an increase of 0.7% for

RCP4.5 (Silveira etal., 2021a).

It is important to note that the assessed studies typically take an

observed epidemiological relationship and apply future temperature

projections (often derived from regional climate projections) to

these relationships. Because the relationships between temperature

and CVD deaths are inﬂuenced by both climatic and non-climatic

factors (such as population ﬁtness and aging), future projections are

highly sensitive to assumptions about interactions between climate,

population characteristics and adaptation pathways. Changes in air

quality because of climate change are an additional important factor.

For example, an assessment of future annual and seasonal excess

mortality from short-term exposure to higher levels of ambient

ozone in Chinese cities under RCP8.5 projected approximately 1,500

excess annual CVD deaths in 2050 (Chen etal., 2018). To the extent

possible, the relationships reported above reﬂect changes derived from

changes in heat exposure driven by climate change and not changes in

population demographics or air pollution exposure.

Climate change could impact CVD through other pathways, including

exposure to ﬁne dust. For example, adult mortality attributable to ﬁne

dust exposure in the American southwest could increase by 750 deaths

–1

(a 130% increase over baseline) by the end of the century under

RCP8.5 (Achakulwisut etal., 2018).

7.3.1.8 Future Risks Related to Maternal, Foetal and Neonatal

Health

Additional research is needed on future impacts of climate change

on maternal, foetal and neonatal health. Maternal heat exposure is a

risk factor for several adverse maternal, foetal and neonatal outcomes

(Kuehn and McCormick, 2017), including foetal growth (Sun et al.,

2019) and congenital anomalies (Haghighi et al., 2021). There is

very limited research on this subject, an exception being Zhang etal.

(2020), which projected a 34% increase in congenital health disease

risk in the USA in 2025 and 2035 based on increased maternal extreme

heat exposure.

7.3.1.9 Future Health Risks Related to Food, Diets and Nutrition

7.3.1.9.1 Malnutrition

Climate change is projected to exacerbate malnutrition (high

conﬁdence). Moderate and severe stunting in children less than ﬁve

years of age was projected for 2030 across 44 countries to be an

additional 570,000cases under a prosperity and low climate change

scenario (RCP2.6) to one million cases under a poverty and high climate

change scenario (RCP8.5), with the highest effects in rural areas

(Lloyd, 2018). Future DALYs lost due to protein-energy undernutrition

and micronutrient deﬁciencies without climate change have been

projected to increase between 2010 and 2050 by over 30million; with

climate change (RCP8.5), DALYs were projected to increase by nearly

10%, with the largest increases in Africa and Asia (Sulser etal., 2021).

The projected risks of hunger and childhood underweight vary under

the ﬁve SSPs, with population growth, improvement in the equality of

food distribution and income-related increases in food consumption

inﬂuencing future risks (Ishida etal., 2014; Hasegawa etal., 2015). A

review of 57studies projecting global food security to 2050 under the

SSPs concluded that global food demand was expected to increase by

35–56% between 2010 and 2050 (van Dijk etal., 2021). In the same

review, estimates of the change in population at risk of hunger by

2050 range between −91 to +8% if climate change is not considered

and between -91 to +30% if climate change is considered, with the

inclusion of climate change not leading to statistically signiﬁcant

differences in projections (van Dijk etal., 2021).

7.3.1.9.2 Climate Change, Carbon Dioxide, Diets and Health

Climate change could further limit equitable access to affordable,

culturally acceptable, and healthy diets (high conﬁdence). Climate

impacts on agricultural production and regional food availability will

affect the composition of diets, which can have major consequences for

health. Variable by region and context, healthy diets are an outcome

of the four inter-connected domains of sustainable food systems,

namely ecosystems, society, economics and health (Drewnowski etal.,

2020; Fanzo et al., 2020). Climate change limits the potential for

healthy diets through adverse impacts on natural and human systems

that are disproportionately experienced by low-income countries

and communities (FAO etal., 2021). Climate-driven droughts, ﬂoods,

storms, wildﬁres and extreme temperatures reduce food production

potential by diminishing soil health, water security and biological and

genetic diversity (Macdiarmid and Whybrow, 2019). Models project

that climate-related reductions in food availability, speciﬁcally fruit

and vegetables, could result in an additional 529,000 deaths a year by

2050 (Springmann etal., 2016b).

Diets reliant on marine ﬁsheries and ﬁsh also face complex climate-

driven challenges (Hollowed et al., 2013). Rapidly warming oceans

(Cheng etal., 2020) limit the size of many ﬁsh and hamper their ability

to relocate or adapt; many commonly consumed ﬁsh, like sardines,

pilchards and herring, could face extinction due to these pressures

(Avaria-Llautureo et al., 2021). Other ﬁsheries models project end-

of-century pollock and Paciﬁc cod ﬁsheries decreasing by >70% and

>35% under RCP8.5 (Holsman etal., 2020). Climate-driven increases

1098

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

in marine mercury concentrations (Booth and Zeller, 2005) and harmful

algal blooms (Jardine etal., 2020) could impact dietary quality and

human health.

Global crop and economic models project higher cereal prices of up

to 29% by 2050 under RCP6.0, resulting in an additional 183million

people in low-income households at risk of hunger (Hasegawa etal.,

2018). Climate impacts on human health disrupt agricultural labour,

food supply chain workers and ultimately regional food availability

and affordability. A recent meta-analysis focused on sub-Saharan

Africa and Southeast Asia combined metrics of heat stress and labour

to project that a 3°C increase in global mean temperature, without

adaptation or mechanisation, could reduce agricultural labour capacity

by 30–50%, leading to 5% higher crop prices and a global welfare loss

of USD136billion (de Lima etal., 2021).

The nutritional density, including protein content, micronutrients and

B-vitamins, of wheat, rice, barley and other important food crops is

negatively affected by higher CO

concentrations (very high conﬁdence)

(Mbow, 2019 ; Smith and Myers, 2018). Projections indicate negative

impacts on human nutrition by rising CO

concentrations by mid- to

late-century (Medek etal., 2017; Smith and Myers, 2018; Weyant etal.,

2018; Zhu etal., 2018; Beach etal., 2019). Staple crops are projected to

have protein and mineral concentrations decreased by 5–15% and B

vitamins up to 30% when the concentrations of CO

double above pre-

industrial levels (Ebi and Loladze, 2019; Beach etal., 2019; Smith and

Myers, 2018). Without changes in diets and accounting for nutrient

declines in staple crops, a projected additional 175 million people

could be zinc deﬁcient and an additional 122 million people could

become protein deﬁcient (Smith and Myers, 2018). Weyant etal. (2018)

projected that CO

-related reductions in crop zinc and iron levels

could result in 125.8million DALYs lost globally, with Southeast Asian

and sub-Saharan African countries most affected. Zhu et al. (2018)

estimated 600million people at risk from reductions in the protein,

micronutrient and B-vitamin content of widely grown rice cultivars in

Southeast Asia.

The combined effect of CO

and rising temperatures because of climate

change could result in a 2.4–4.3% penalty on expected gains by mid-

century in nutritional content because of technology change, market

responses and the fertilisation effects of CO

on yield (Beach etal.,

2019). These penalties are expected to slow progress in achieving

reductions in global nutrient deﬁciencies, disproportionately affecting

countries with high levels of such deﬁciencies.

7.3.1.10 Projected Impacts on Harmful Algal Blooms,

Mycotoxins, Aﬂatoxins and Chemical Contaminants

Harmful algal blooms are projected to increase globally, thus

increasing the risk of seafood contamination with marine toxins (high

conﬁdence) (European Food Safety Authority etal., 2020; Gobler etal.,

2017; Barange etal., 2018; IPCC, 2019b; Wells etal., 2020). Climate

change impacts on oceans could generate increased risks of ciguatera

poisoning in some regions (medium conﬁdence). Studies suggest

that rising sea surface temperatures could increase rates of ciguatera

poisoning in Spain (Botana, 2016) and other parts of Europe (European

Food Safety Authority etal., 2020).

Mycotoxins and aﬂatoxins may become more prevalent due to climate

change (medium agreement, low evidence). Models of aﬂatoxin

occurrence in maize under climate change scenarios of +2°C and

+5°C in Europe over the next 100 years project that aﬂatoxin B1

may become a major food safety issue in maize, especially in Eastern

Europe, the Balkan Peninsula and the Mediterranean regions (Battilani,

2016). The occurrence of toxin-producing fungal phytopathogens has

the potential to increase and expand from tropical and subtropical

regions into regions where such contamination does not currently

occur (Battilani, 2016).

Climate change may alter regional and local exposures to anthropogenic

chemical contaminants (medium agreement, low evidence). Changes

in future occurrences of wildﬁres could lead to a 14% increase in

global emissions of mercury by 2050, depending on the scenarios used

(Kumar etal., 2018a). Mercury exposure via consumption of ﬁsh may

be affected by warming waters. Warming trends in the Gulf of Maine

could increase the methyl mercury levels in resident tuna by 30%

between 2015 and 2030 (Schartup etal., 2019). An observed annual

3.5% increase in mercury levels was attributed to ﬁsh having higher

metabolism in warmer waters, leading them to consume more prey.

The combined impacts of climate change and the presence of arsenic in

paddy ﬁelds are projected to potentially double the toxic heavy metal

content of rice in some regions, potentially leading to a 39% reduction

in overall production by 2100 under some models (Muehe etal., 2019).

7.3.1.11 Future Risks Related to Mental Health and Well-Being

Climate change is expected to have adverse impacts on well-being,

some of which will become serious enough to threaten mental health

(very high conﬁdence). However, changes (Hayes and Poland, 2018) in

extreme events due to climate change, including ﬂoods (Baryshnikova,

2019), droughts (Carleton, 2017) and hurricanes (Kessler etal., 2008;

Boscarino etal., 2013, Boscarino etal., 2017; Obradovich etal., 2018),

which are projected to increase due to climate change, directly worsen

mental health and well-being and increase anxiety (high conﬁdence).

Projections suggest that sub-Saharan African children and adolescents,

particularly girls, are extremely vulnerable to negative direct and

indirect impacts on their mental health and well-being (Atkinson and

Bruce, 2015; Owen etal., 2016). The direct risks are greatest for people

with existing mental disorders, physical injuries, and compromised

respiratory, cardiovascular and reproductive systems, with indirect

impacts potentially arising from displacement, migration, famine and

malnutrition, degradation or destruction of health and social care

systems, conﬂict, and climate-related economic and social losses (high

to very high conﬁdence) (Burke etal., 2018; Curtis etal., 2017; Hayes

etal., 2018; Serdeczny etal., 2017; Watts etal., 2019). Demographic

factors increasing vulnerability include age, gender and low

socioeconomic status, though the effect of these will vary depending

on the speciﬁc manifestation of climate change; overall, climate change

is predicted to increase inequality in mental health across the globe

(Cianconi et al., 2020). Based on evidence assessed in Section 7.2,

future direct impacts of increased heat risks and associated illnesses

can be expected to have negative implications for mental health and

well-being, with outcomes being highly mediated by adaptation, but

there are no assessable studies that quantify such risks. There may

be some beneﬁts to mental health and well-being associated with

1099

Health, Wellbeing and the Changing Structure of Communities Chapter 7

fewer very cold days in the winter; however, research is inconsistent.

Any positive effect associated with reduced low-temperature days is

projected to be outweighed by the negative effects of increased high

temperatures (Cianconi etal., 2020).

Human behaviours and systems will be disrupted by climate change

in a myriad of ways, and the potential consequences for mental

health and well-being are correspondingly large in number and

complex in mechanism (high conﬁdence). For example, climate

change may alter human physical activity and mobility patterns, in

turn producing alterations in the mental health statuses promoted by

regular physical activity (Obradovich and Fowler, 2017; Obradovich

and Rahwan, 2019). Climate change may affect labour capacity,

because heat can compromise the ability to engage in manual labour

as well as cognitive functioning, with impacts on the economic

status of individual households as well as societies (Kjellstrom

et al., 2016; Liu, 2020). Migrations and displacement caused by

climate change may worsen the well-being of those affected (Vins

et al., 2015; Missirian and Schlenker, 2017). Climate change is

expected to increase aggression through both direct and indirect

mechanisms, with one study predicting a 6% increase in homicides

globally for a 1°C temperature increase, although noting signiﬁcant

variability across countries (Mares and Moffett, 2016). Broad

societal outcomes such as economic unrest, political conﬂict or

governmental dysfunction assessed in Section7.3.5 may undermine

the mental health of populations in the future (medium conﬁdence).

Food insecurity presents its own severe risks for mental health and

cognitive function (Jones, 2017).

7.3.2 Migration and Displacement in a Changing Climate

Future changes in climate-related migration and displacement are

expected to vary by region and over time according to: (a) region-speciﬁc

changes in climatic drivers, (b) changes in the future adaptive capacity

of exposed populations, (c) population growth in areas most exposed

to climatic risks and (d) future changes in mediating factors such as

international development and migration policies (high agreement,

medium evidence) (Gemenne and Blocher, 2017; Cattaneo etal., 2019;

McLeman, 2019). This section assesses future risks associated with

changes in the frequency and/or severity of storms, ﬂoods, droughts,

extreme heat, wildﬁres and other events assessed in Section7.2 that

currently affect migration and displacement patterns, as well as the

impacts of emerging hazards, including average temperature increases

that may affect the habitability of settlements in arid regions and the

tropics, and sea level rise and associated hazards that threaten low-

lying coastal settlements. Studies assessed here consider projected

changes in future exposure to hazards over a variety of geographical

and temporal periods, with some considering changes in population

numbers in exposed areas. The uneven distribution of exposure of age

cohorts is typically overlooked in existing research. For example, people

younger than age 10 in the year 2020 are projected to experience a

nearly four-fold increase in extreme events under 1.5°C of global

warming and a ﬁve-fold increase under 3°C warming; such increases

in exposure would not be experienced by a person of the age of 55 in

2020 in their remaining lifetime under any warming scenario (Thiery

etal., 2021).

7.3.2.1 Region-Speciﬁc Changes in Climatic Risks

As outlined in 7.2, the most common drivers of observed climate-

related migration and displacement are extreme storms (particularly

tropical cyclones), ﬂoods and droughts (high conﬁdence). The future

frequency and/or severity of such events due to anthropogenic climate

change are expected to vary by region according to future GHG

emission pathways (Naik et al 2021; Regional Chapters, this report),

with there being an increased potential for compound effects of

successive or multiple hazards (e.g., tropical storms accompanied by

extreme heat events (Matthews etal., 2019)). Table7.2 summarises

anticipated changes in future migration and displacement risks due to

sudden-onset climate events by region (and by sub-regions for Africa

and Asia, where climatic risks vary within the region).

In low-lying coastal areas of most regions, future increases in mean

sea levels will amplify the impacts of coastal hazards on settlements,

including erosion, inland penetration of storm surges and groundwater

contamination by salt water, and eventually lead to inundation of very

low-lying coastal settlements (high conﬁdence) (Diaz, 2016; Hauer

etal., 2016; Neumann etal., 2015; Rahman etal., 2019; IPCC, 2019a).

Projections of the number of people at risk of future displacement by

sea level rise range from tens of millions to hundreds of millions by the

end of this century, depending on (a) the sea level rise scenario or RCP

selected, (b) projections of future population growth in exposed areas

and (c) the criteria used for identifying exposure. These latter measures

can include estimates of populations situated within selected elevations

above sea level (with 1 m, 2 m and 10 m being common parameters),

populations situated in 1-in-100 year ﬂoodplains or populations in

areas likely to be entirely inundated under speciﬁc RCPs (Neumann

etal., 2015; Hauer etal., 2016; Merkens etal., 2018; McMichael etal.,

2020; Hooijer and Vernimmen, 2021). As an illustrative example, an

estimated 267million people (error range = 197–347million at 68%

conﬁdence level) worldwide lived within 2 m of sea level in 2020, 59%

of whom reside in tropical regions of Asia (Hooijer and Vernimmen,

2021). At a 1 m increase in sea level and holding coastal population

numbers constant, the number of people worldwide living within 2 m

of sea level expands to 410million (error range = 341–473million).

However, it is unlikely that coastal population growth rates will remain

constant at global or regional scales in future decades. At present,

coastal cities in many regions have relatively high rates of population

growth due to the combined effects of in-migration from other regions

and natural increase, with coastal areas of Africa having the highest

projected future population growth rates (Neumann et al., 2015;

Hooijer and Vernimmen, 2021; Box7.5). Further complicating future

estimates is that many large coastal cities are situated in deltas with

high rates of subsidence, meaning that locally experienced changes in

relative sea level may be much greater than sea level rise attributable

to climate change, thereby further increasing the number of people

exposed (Edmonds etal., 2020; Nicholls etal., 2021).

Sea level rise is not presently a signiﬁcant driver of migration in

comparison with hazards assessed in Section7.2.6, but it has been

attributed as a factor necessitating the near-term resettlement of small

coastal settlements in Alaska, Louisiana, Fiji, Tuvalu and the Carteret

Islands of Papua New Guinea (Marino and Lazrus, 2015; Connell, 2016;

Hamilton etal., 2016; Nichols, 2019). In coastal Louisiana, communities

1100

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Table7.2 | Projected changes in sudden-onset climate events associated with migration and displacement by region.

Region

Main directions of current migration

ﬂows (from Abel and Sander (2014))

Current climatic drivers of

migration and displacement

(Section7.2.6.1)

Expected changes in drivers (including conﬁdence state-

ments) from IPCC WGI 2021 Technical Summary, 4.3.1–4.3.2

Asia

– East and Southeast Asia: within countries and

between countries within same region.

– South and central Asia: within countries and

between countries within same region; from

south Asia to Middle East, North America,

Europe.

– West Asia: within countries and between

countries within the same region; to Europe

Floods, extreme storms, extreme heat

Increased risk of ﬂooding in East, north, south and Southeast Asia due

to increases in annual mean precipitation (high conﬁdence) and extreme

precipitation events in East, south, west central, north and Southeast Asia

(medium conﬁdence); uncertainty regarding future trends in cyclones

(current trend = decreased frequency, increased intensity); higher average

temperatures across region (high conﬁdence)

Africa

– Within countries and between countries

within the same region; to Europe and the

Middle East

Floods, droughts, extreme heat

Decrease in total annual precipitation in northernmost and

southernmost parts of Africa (high conﬁdence); west-to-east pattern of

decreasing-to-increasing annual precipitation in west Africa and east

Africa (medium conﬁdence); increased risk of heavy precipitation events

that trigger ﬂooding, across most parts of Africa (medium conﬁdence);

increased aridity and drought risks in north Africa, southern Africa and

western parts of west Africa (medium-high conﬁdence)

Europe

– Within countries and between countries in

same region

Floods

Increased risk of ﬂoods across all areas of Europe except Mediterranean

areas (high conﬁdence); higher risks of drought and ﬁre weather in

Mediterranean areas (high conﬁdence)

North America

– Within countries and between countries in

same region

Floods, tropical cyclones (US Atlantic

and Caribbean coast), tornadoes,

wildﬁres

Increased frequency of heavy precipitation events across most areas

(high conﬁdence); tropical cyclones to become more severe (medium

conﬁdence); increased risk of drought and ﬁre weather in central and

western North America

Central and

South America

– Within countries and between countries in

same region; to North America, Europe

Floods (Central and South America),

extreme storms (Central America)

Increases in mean annual precipitation and extreme precipitation events

with higher risks of ﬂoods in most areas of South America (medium

conﬁdence); increased risk of droughts in northeastern and southern

South America and northern Central America (medium conﬁdence);

tropical cyclones becoming more extreme (medium conﬁdence)

Australasia – Displacement within countries Wildﬁres

Increases in ﬁre weather across Australia and New Zealand (medium

conﬁdence)

Small island

states

– Within and between countries in same region

(e.g., Paciﬁc Islands to Australia and New

Zealand; Caribbean islands to USA)

Extreme storms Potentially fewer but more extreme tropical cyclones (medium conﬁdence)

tend to resist leaving exposed settlements until approximately 50% of

available land has been lost (Hauer etal., 2019). Movements away from

highly exposed areas may have longer-term demographic implications

for inland settlements (Hauer, 2017), but this requires further study.

Based on the available empirical evidence, sea level rise does not

appear to currently be a primary motivation for international migration

originating in small island states in the Indian and Paciﬁc Oceans;

rather, economic considerations and family reuniﬁcation appear to be

the dominant drivers (McCubbin etal., 2015; Stojanov and Du, 2016;

Heslin, 2019; Kelman etal., 2019). However, climatic drivers of migration

are anticipated to take on a much greater causal role in migration

decisions in coming decades (Thomas etal., 2020) and may discourage

return migration to small island states (van der Geest etal., 2020).

Even under best-case sustainable development scenarios, rising sea

levels and associated hazards create risks of involuntary displacement

in low-lying coastal areas and should be expected to generate a need

for organised relocation of populations where protective infrastructure

cannot be constructed (Horton and de Sherbinin, 2021; Hamilton etal.,

2016). In high emissions scenarios, low-lying island states may face

the long-term risk of becoming uninhabitable, creating the potential

for a new phenomenon of climate-induced statelessness (Piguet, 2019;

Desai etal., 2021).

Increased frequency of extreme heat events and long-term increases

in average temperatures pose future risks to the habitability of

settlements in tropical and subtropical regions, and may in the

long term affect migration patterns in exposed areas, especially

under high emissions scenarios (medium agreement, low evidence).

Greater research into the speciﬁc dynamics between extreme heat

and population movements is required in order to make an accurate

assessment of this risk. Recent studies suggest that future increases

in average temperatures could expose populations across wide areas

of the tropics and subtropics to ambient temperatures for extended

periods each year that are beyond the threshold for human habitability

(Pal and Eltahir, 2016; Im etal., 2017; Xu etal., 2020). This effect would

be ampliﬁed in urban settings where heat-island effects occur and

create a heightened need for air conditioning and other adaptation

measures. In addition to risks associated with average temperature

changes, Dosio etal. (2018) project that at 1.5°C warming, between

9% and 18% of the global population will be regularly exposed to

extreme heat events at least once in ﬁve years, with the exposure rate

nearly tripling with 2°C warming. How these changes in exposure to

high temperatures will affect future migration patterns, particularly

among vulnerable groups, will depend heavily on future adaptation

responses (Horton and de Sherbinin, 2021). Multiple country-level

1101

Health, Wellbeing and the Changing Structure of Communities Chapter 7

studies assessed in Section7.2 observe existing associations between

extreme heat, its impacts on agricultural livelihoods and changes in

rural-to-urban migration ﬂows in parts of south Asia and sub-Saharan

Africa. A study conducted in Indonesia, Malaysia and the Philippines

suggests that an increased risk of heat stress would likely inﬂuence

migration intentions of signiﬁcant numbers of people (Zander etal.,

2019).

7.3.2.2 Interactions with Non-climatic Determinants and

Projections of Future Migration Flows

Only a very small number of studies have attempted to make systematic

projections of future regional or global migration and displacement

numbers under climate change. Key methodological challenges for

making such projections include the availability of reliable data on

migration within and between countries, deﬁnitional ambiguity in

distinguishing climate-related migration from migration undertaken for

other reasons, and accounting for the future inﬂuence of non-climatic

factors. The most reliable example of such studies to date is a World

Bank report by Rigaud etal. (2018) that generated projections of future

internal population displacements in south Asia, sub-Saharan Africa

and Latin America by 2050 using multiple climate and development

scenarios, resulting in a very large range of possible outcomes (from

31 to 143million people being displaced, depending on assumptions).

An important outcome is the study’s emphasis on how the potential

for future migration and displacement will be strongly mediated by

socioeconomic development pathways in low- and middle-income

countries. Hoffmann etal. (2020) used meta-regression-based analyses

to project that future environmental inﬂuences on migration are likely

to be greatest in low- and middle-income countries in Latin America

and the Caribbean, sub-Saharan Africa, the Middle East and most of

continental Asia.

Research reviewed in AR4 and AR5 observed that at higher rates

of socioeconomic development, the in situ adaptive capacity of

households and institutions rises, and climatic inﬂuences on migration

correspondingly decline. Recent evidence adds further support for such

conclusions (high conﬁdence) (Kumar etal., 2018b; Mallick, 2019; Gray

etal., 2020; Box7.5). Population growth rates are currently highest

in low-income countries (UN DESA Population Division, 2019), many

of which have high rates of exposure to climatic hazards associated

with population displacement, further emphasising the importance of

socioeconomic development and adaptive capacity-building. Although

country-speciﬁc scenarios for socioeconomic development and

population are embedded in SSPs, research into future migration ﬂows

under climate change has not made great use of these. One of the few

studies to do so found that safe and orderly international migration

tends to increase wealth at regional and global scales in all SSP

narratives, which in turn reduces income inequality between countries

(Benveniste et al., 2021). International barriers to safe and orderly

migration may potentially impede progress towards attainment of the

objectives described in the SDGs and increase exposure to climatic

hazards in low- and middle-income countries (McLeman, 2019;

Benveniste etal., 2020).

Box7.5 | Uncertainties in projections of future demographic patterns at global, regional and

national scales

Projections of future numbers of people exposed to climate change-related hazards described in this chapter and elsewhere in this

report are heavily inﬂuenced by assumptions about population change over time at global, regional and national scales. One challenge

concerns global and regional variability of baseline data for current populations, which is typically aggregated from national censuses

that vary considerably in terms of frequency, timing and reliability, especially in low-income countries. A number of gridded mapping

dataset initiatives emerged in recent years to support population–environment modelling research at global and regional levels, common

ones being the Gridded Population of the World, the Global Rural Urban Mapping Project, and LandScan Global Population dataset

(McMichael etal., 2020). For future population projections at national levels, researchers commonly draw upon data generated by the

Population Division of the United Nations Department of Economic and Social Affairs, which publishes periodic projections for future

fertility, mortality, and international migration rates for over 200countries, the most recent projections being for the 2020 to 2100

period (UN DESA Population Division, 2019). There have been debates among demographers regarding the precision of DESA projections,

and whether these overestimate or underestimate future population growth in some regions (Ezeh etal., 2020). Population growth

rates are highly inﬂuenced by socioeconomic conditions, meaning that future population levels at local, national and regional scales

are likely to respond to relative rates of progress towards meeting the Sustainable Development Goals (Abel etal., 2016). The Shared

Socioeconomic Pathways (SSPs) used in climate impacts and adaptation research include a variety of assumptions about future mortality,

fertility and migration rates and provide a range of population growth scenarios that diverge after the year 2030 according to future

development trajectories (Samir and Lutz, 2017) and are then further modiﬁed and downscaled by researchers for national-level studies.

Understanding future risks of climate change will beneﬁt from continued efforts by the international community to collect and share

data on observed population numbers and trends, and to work towards better projected data for population characteristics that strongly

inﬂuence vulnerability to climate risks, such as gender, age and indigeneity.

1102

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

7.3.3 Climate Change and Future Risks of Conﬂict

Climate change may increase susceptibility to violent conﬂict, primarily

intra-state conﬂicts, by strengthening climate-sensitive drivers of

conﬂict (medium conﬁdence). Section 7.2.7 described how climate

variability and extremes affect violent conﬂict through food and water

insecurity, loss of income and loss of livelihoods. Risks are ampliﬁed

by insecure land tenure, competing land uses and weather-sensitive

economic activities when they occur in the context of weak institutions

and poor governance, poverty and inequality (Section7.2.7). These

known, climate-sensitive risk factors allow projections of where

conﬂict is more likely to arise or worsen under climate change impacts

(see Chapters 1, 4, 5, 6 and 16) (Mach etal., 2020). However, there

is also the potential for new causal pathways to emerge as climate

changes beyond the variability observed in available datasets and

adaptation limits are met (Theisen, 2017; Mach et al., 2019; von

Uexkull and Buhaug, 2021).

Future violent conﬂict risk is highly mediated by socioeconomic

development trajectories (high conﬁdence). Development trajectories

that prioritise economic growth, political rights and sustainability are

associated with lower conﬂict risk (medium conﬁdence, low evidence).

Hegre etal. (2016) forecast future conﬂict under the SSPs and found

that SSP1, which prioritises sustainable development, is associated

with lower risks of conﬂict. Using data from sub-Saharan Africa,

Witmer et al. (2017) forecast conﬂict along the SSPs and ﬁnd that

any increases in conﬂict that may be associated with climate change

could be offset by increases in political rights. Strong predictors of

future conﬂict are a recent history of conﬂict, large populations and

low levels of socioeconomic development (Hegre and Sambanis, 2006;

Blattman and Miguel, 2010).

Increases in conﬂict-related deaths with climate change have been

estimated but results are inconclusive (high agreement, medium

evidence). Some studies attempted to attribute observed conﬂict

outbreaks to changes in the physical environment and quantify future

conﬂict risk associated with climate change (von Uexkull and Buhaug,

2021; Theisen, 2017). Burke etal. (2015b) concluded that with each

one standard deviation increase in temperature, inter-personal conﬂict

increased by 2.4% and inter-group conﬂict by 11.3%. However, the

statistical methods have been criticised for under-representing the

known role that socioeconomic conditions and conﬂict history play

in determining the prevalence of violence (Buhaug etal., 2014; van

Weezel, 2019; Abel etal., 2019). Forecasting armed conﬂict is used

as a heuristic policy tool rather than a representation of the future

(Cederman and Weidmann, 2017) and forecasts have limitations. For

example, what constitutes and is experienced as hazards and as drivers

of conﬂict will shift over time as societies adapt to climate change

(Roche etal., 2020). The SSPs assume economic convergence between

countries and do not reﬂect growth disruptions (e.g., commodity price

shocks) that are often a key conﬂict risk factor (Dellink etal., 2017;

Buhaug and Vestby, 2019; Hegre etal., 2021).

Asia represents a key region where the peace, vulnerability and

development nexus has been analysed. In central, south and Southeast

Asia, there are large numbers of people exposed to changing climate

(Busby etal., 2018; Vinke etal., 2017; Reyer etal., 2017). South Asia

is one of the less peaceful regions in the world due to intra-state

communal conﬂict, international military conﬂict and political tension

(Wischnath and Buhaug, 2014; Huda, 2021), and many of the factors

that drive conﬂict risk (e.g. large populations with high levels of

inequality) are present (Nordqvist and Krampe, 2018). Despite these

risks, studies in this region also support the case for environmental

peacebuilding and resource sharing, as it relates to transboundary

water sharing (Berndtsson and Tussupova, 2020; Huda and Ali, 2018;

Section4.3.6; Section7.4.5.2).

There is little evidence of weather-related impacts on conﬂict risk or

prevalence, but the region is under-studied in general (Wischnath and

Buhaug, 2014; Nordqvist and Krampe, 2018). Climate stressors may

have contributed in part to local-level conﬂicts in Bangladesh and

Nepal (Sultana etal., 2019) and intensiﬁed water use conﬂict in peri-

urban areas (Roth etal., 2019). In the future there is the potential

for climate change to stretch the effectiveness of transboundary

water agreements by raising regional geopolitical tensions (Atef

etal., 2019; Scott etal., 2019) or to generate water use conﬂicts

between hydropower and irrigation within countries (Jalilov etal.,

2018). Climate change may have an impact on conﬂict by affecting

food security (Caruso et al., 2016; Raghavan et al., 2019). There

may be greater military involvement in humanitarian response to

cyclones, ﬂooding and to other impacts of climate change that might

contribute to increased instability (Pai, 2008; Busby and Krishnan,

2017).

7.4 Adaptation to Key Risks and Climate

Resilient Development Pathways

With proactive, timely and effective adaptation, many observed and

projected risks for human health and well-being, health systems

and those associated with migration and conﬂict can be reduced

or potentially avoided (high conﬁdence). Given the key health risks

identiﬁed in this chapter, adaptation that increases resilience and

sustainability will require moving beyond incremental adaptation to

sustained, adaptive management (Ebi, 2011; Hess etal., 2012) with

the goal of transformative change for integrated protection of human,

animal and ecosystem health. This includes differentiating adaptation

to climate variability from adaptation to climate change (Ebi and Hess,

2020). Health adaptation efforts are increasingly aiming to transition

to building climate-resilient and environmentally sustainable health

systems (WHO, 2015b; WHO, 2020a) and healthcare facilities,

emphasising service delivery including climate-informed health policies

and programmes; management of the environmental determinants of

health; emergency preparedness and management; health information

systems such as health and climate research, integrated risk monitoring

and early warning systems; and vulnerability, capacity and adaptation

assessments (Marinucci etal., 2014; Mousavi etal., 2020; WHO 2015a;

Centres for Disease Control , 2019; WHO, 2020a).

Migration can contribute to or work against adaptation goals and

progress, depending on the circumstances under which it occurs.

Policies that support safe and orderly movements of people, protect

migrant rights, and facilitate ﬂows of ﬁnancial and other resources

between sending and receiving communities are consistent with

1103

Health, Wellbeing and the Changing Structure of Communities Chapter 7

adaptive capacity-building and building sustainability and are part of

CRDPs (section 7.4.4).

Adaptation to prevent climate change from exacerbating conﬂict risk

involves meeting development objectives encapsulated in the SDGs.

Conﬂict-sensitive adaptation and climate-sensitive peacebuilding

offer promising avenues to addressing conﬂict risk, but their efﬁcacy

is yet to be demonstrated through effective monitoring and evaluation

(Gilmore et al., 2018). Associations between environmental factors

and conﬂict are weak in comparison to socioeconomic and political

drivers. Therefore, meeting the SDGs, including Goal 16 on peace,

justice and strong institutions represent unambiguous pathways to

reducing conﬂict risk under climate change (Singh and Chudasama,

2021). Actively pursuing peace rather than taking conﬂict for granted

(Barnett, 2019), improving focus on gender within peacebuilding (Dunn

and Matthew, 2015; UNEP, 2021) and understanding how natural

resources and their governance interact with peacebuilding (Krampe

etal., 2021) present key elements of CRDPs for sustainable peace.

As documented across this chapter, there is a large adaptation deﬁcit

for health and well-being, with climate change causing avoidable

injuries, illnesses, disabilities, diseases and deaths (high conﬁdence).

Implementation of health adaptation has been incremental because

of signiﬁcant constraints, primarily relating to ﬁnancial and human

resources and because of limited research funding on adaptation

(Berrang-Ford et al., 2021). Current global investments in health

adaptation are insufﬁcient to protect the health of populations and

communities (high conﬁdence) from most climate-sensitive risks, with

large variability across and within countries and regions (UNEP, 2018).

Climate change adaptation in health is <1% of international climate

ﬁnance despite health being a priority sector in 54% of NDCs featuring

adaptation (UNEP, 2018).

As climate change progresses and the likelihood of dangerous risks

to human health continue to increase, there will be greater pressure

for more transformational changes to health systems to reduce future

vulnerabilities and limit further dangerous climate change (Ebi etal.,

2021a). Transformational resilience would need parallel investments

in social and health protections, including achieving the SDGs,

coupled with investments in mitigation (Ebi and Hess, 2020). Further,

investments in mitigating GHG emissions will not only reduce risks

associated with dangerous climate change but will improve population

health and well-being through several salutary pathways.

This chapter section identiﬁes and assesses speciﬁc elements in

adapting to the risks identiﬁed in 7.2 and 7.3 and the opportunities for

fostering sustainability and pursuing CRDPs.

7.4.1 Adaptation Solution Space for Health and Well-

Being

The solution space is the space within which opportunities and

constraints determine why, how, when and who adapts to climate

change (Chapter 1). There is increased understanding of exposure

and vulnerabilities to climate variability and change, the capacities to

manage the health risks, the effectiveness of adaptation (including a

growing number of lessons learned and best practices), and the co-

beneﬁts of mitigation policies and technologies (high conﬁdence).

Effectively preparing for and managing the health risks of climate

change requires considering the multiple interacting sectors that

affect population health and the effective functioning of health

systems (high conﬁdence). Given the wide range of causal pathways

through which climate change affects environmental and social

systems resulting in health impacts, a systems-based approach can

promote identifying, implementing and evaluating solutions that

support population health and health systems in the short and

longer term (high agreement, medium evidence). Such an approach

provides insights into policies and programmes that promote health

and well-being via multiple sectors (e.g., water and food safety and

security) and can ensure that health policies do not have adverse

consequences in other sectors (WHO, 2015b; Ebi and Otmani del

Barrio, 2017; Wright etal., 2021).

Figure 7.11 illustrates the context within which risks to health

outcomes and health systems emerge because of climate change.

The ﬁgure presents the emergence of risk from interactions between

speciﬁc types of climatic hazards, the exposure and vulnerability to

those hazards, and the responses taken within the health sector. The

ﬁgure also illustrates how health risks are situated within larger

interactions between the health system and other sectors and

systems, with underlying enabling conditions making adaptation

and transformation possible. Within this context, response options

can decrease the impacts of climate change on human health,

well-being and health systems by (a) reducing exposure to climate-

related hazards; (b) reducing vulnerability to such hazards and

approaches are described as ‘Lateral Public Health’ and emphasise

the importance of involving community members and stakeholders in

the planning and coordination of activities (Semenza, 2021; Semenza,

2011). Lateral public health strives for community engagement (e.g.,

through access to technology in decision-making, such as low-cost

air sensors for wildﬁre smoke) in preparedness and response.

Effective health risk management incorporates the magnitude and

pattern of future climate risks as well as potential changes in factors

that determine vulnerability and exposure to climate hazards, such as

determinants of healthcare access, demographic shifts, urbanisation

patterns and changes in ecosystems (very high conﬁdence). Climate

change is associated with shocks and stresses that can affect the capacity

and resilience of health systems and healthcare facilities (WHO, 2020a).

Figure7.12 illustrates some possible extents to which the capacity of

health systems could be reduced when exposed to a stress or shock,

and possible pathways forward, from collapse to transformation. The

subsequent sections of this chapter assess adaptation and mitigation

options to facilitate building the resilience of health systems and

healthcare facilities to recover better than before or to transform.

1104

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Health systems capacity and resilience to climate change-related shocks and stresses

Above average

Normal

Below average

Low

Shock

Stress

Transform

Recover better than before

Recover to pre-event state

Recover but worse than befor

Collapse

Time

Climate change adaptation

Prevention, preparednessResponseRecovery

Performance drop

Health system capacity Health system resilience

Vulnerability

Exposure

Hazard

Risk

Figure7.12 | Health systems capacity and resilience to climate change-related shocks and stresses. From WHO (2020).

Adaptation responses to climatic risks

Adaptation in other sectors

Vulnerability

Exposure

Hazard

Risk

Sustainable

livelihoods

Ch. 8

Biodiversity

Ch. 2; CCP 1

Ocean &

coastal systems

Ch. 3

Urban

systems

Ch. 6

Water & food

Ch. 4, 5

Adaptation responses

Can reduce vulnerability

Can reduce exposure

Health sector

adaptation

Figure7.11 | Context within which adaptation responses to climatic risks to health are implemented in the frame of interactions between health and

multiple other sectors.

1105

Health, Wellbeing and the Changing Structure of Communities Chapter 7

7.4.2 Adaptation Strategies, Policies and Interventions

7.4.2.1 Current State of Health Adaptation

Analysis of the NDCs to the Paris Agreement to determine how health

was incorporated, including impacts, adaptation and co-beneﬁts,

concluded that most low- and middle-income countries referred to health

in their NDC (Dasandi etal., 2021). Figure7.13 shows the degree of

health engagement; this engagement is based on indicators measuring

the speciﬁcity and detail of health references within a country’s NDC.

Many vulnerable countries had high engagement of the health sector

in the country NDC. However, this analysis did not determine whether

the ambition expressed was sufﬁcient to address the health adaptation

needs.

The 2018 WHO Health and Climate Change Survey, a voluntary

national survey sent to all 194 WHO member states, to which 101

responded, found that national planning on health and climate change

is advancing, but the comprehensiveness of strategies and plans need

to be strengthened. Implementing action on key health and climate

change priorities remains challenging and multi-sectoral collaboration

on health and climate change policy is evident, with uneven progress

(Watts etal., 2021). Approximately 50% of respondent countries had

developed national health and climate strategies, with over two-thirds

doing so within the preceding ﬁve years, and 48 of 101countries had

conducted a health vulnerability and adaptation assessment (Watts

etal., 2019). However, most countries reported only moderate or low

levels of implementation, with ﬁnancing cited as the most common

barrier due to a lack of information on opportunities, in turn linked to a

lack of connection by health actors to climate change policy processes

and a lack of capacity to participate in national planning. A review of

public health systems in 34countries found that only slightly more

than half considered climate change impacts and adaptation needs

(Berry etal., 2018). Because the health risks of climate change often

vary within a country, sub-national assessments and plans are needed

to help local authorities protect and promote population health in a

changing climate (Aracena etal., 2021; Basel etal., 2020; Schramm

etal., 2020a).

7.4.2.2 Adaptation in Health Policies and Programmes

Health policies were historically not designed or implemented taking

into consideration the risks of climate change and as currently

structured are likely insufﬁcient to manage the changing health

burdens in coming decades (very high conﬁdence). The magnitude

and pattern of future health burdens attributable to climate change,

at least until mid-century, will be determined primarily by adaptation

and development choices. Current and future emissions will play an

increasing role in determining attributable burdens after mid-century.

Increased investment in strengthening general health systems, along

with targeted investments to enhance protection against speciﬁc

climate-sensitive exposures (e.g., hazard early warning and response

systems and integrated vector control programmes for VBDs)

will increase resilience if implemented to at least keep pace with

climate change (high conﬁdence). Investments to address the social

determinants of health can reduce inequities and increase resilience

(high conﬁdence) (Thornton etal., 2016; Marmot etal., 2020; Wallace

etal., 2015; Semenza and Paz, 2021).

Peer-reviewed publications of health adaptation to climate change in

low- and middle-income countries have typically focused on ﬂooding,

rainfall, drought and extreme heat through improving community

resilience, DRR and policy, governance and ﬁnance (Berrang-Ford

etal., 2021; Scheelbeek etal., 2021). Health outcomes of successful

Health engagement score in

Nationally Determined Contributions (NDCs)

across countries

0 1 2 4

Health engagement score

Figure7.13 | Health engagement score in NDCs by country. Figure adapted from Dasandi etal. (2021).

1106

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

adaptation have included reductions in infectious disease incidence,

improved access to water and sanitation and improved food security.

Figure7.14 shows a Sankey diagram of climate hazards, adaptation

responses and health outcomes. The ﬁgure highlights the range of

health adaptation responses that are discussed in more detail earlier

in this chapter and demonstrates the potential health beneﬁt of

adaptation efforts that affect a broad range of health determinants.

Questions of the feasibility and effectiveness of health adaptation

options differ from those in other sectors because public health is a

societal enterprise that cuts across many different spheres of society.

Consequently, there are dependencies that lie outside the jurisdiction

of the health sector. All the health risks of a changing climate currently

cause adverse outcomes, with policies and programmes implemented

in at least some health programmes in some places. Policies and

programmes are continuously modiﬁed to increase effectiveness;

this will need to accelerate in a changing climate. Improvements are

needed as more is understood about disease aetiology, changing

socioeconomic and environmental conditions, obstacles to uptake and

other factors.

Policies and programmes for climate-sensitive health outcomes are

only beginning to incorporate the challenges and opportunities of

climate change, although this is critical for increasing resilience.

The fundamentals of many policies and programmes in a changing

climate will remain the same: implementing infectious disease control

programmes, preventing heat-related mortality and morbidity and

reducing the burden of other climate-related health endpoints, but

activities will need to explicitly account for climate change to continue

to protect health. Even with such attention to climate change, there

are limits to the feasibility and effectiveness of health adaptation

options for extreme heat, controlling emerging infectious diseases and

controlling cascading risk pathways.

As discussed in Sections 1.4.2 and 1.5, an adaptation option is

feasible when it is capable of being implemented by one or more

relevant actors. In the health sector, WHO, the United Nations

Children’s Fund (UNICEF) and other organisations provide technical

expertise to ministries of health, who then provide national to local

healthcare and public health services. Generally, the question is

less of overall feasibility, given the range of potential adaptation

options that have yet to be fully explored and implemented, but

more of readiness to buy into the adaptation efforts required

from health and other sectors. In speciﬁc contexts, feasibility also

depends on governance capacity, ﬁnancial capacity, public opinion

and the distribution of political and economic power (Chapter 17).

In other words, adaptation to climate change is broadly feasible

with adequate investment and engagement, although this has yet

Extreme precipitation and flooding

Drought

Heat

Precipitation variability

Sea-level rise

Ocean acidification

Other climate hazards

Behaviour change

Capacity building

(Micro)Financing

Green infrastructure

and climate-smart agriculture

Information and awareness

Physical infrastructure

Policy options

New technologies

Early warning systems

All cause mortality

Food security

WASH

Infectious disease

Mental health

Non-communicable disease

Health systems

Other health outcomes

Climate hazards Adaptation responses

Health (pathway)

outcomes

Figure7.14 | Sankey diagram of climate hazards, adaptation responses and health outcomes. CSA is climate-smart agriculture. Source: Scheelbeek etal. (2021).

1107

Health, Wellbeing and the Changing Structure of Communities Chapter 7

to materialise, and in speciﬁc contexts feasibility is contingent and

time-varying, and needs to be assessed at national to sub-national

scales. For example, a scoping review in the Paciﬁc region noted the

following areas where further and signiﬁcant investment and support

are needed to increase feasibility of climate and health action: (a)

health workforce capacity development, (b) enhanced surveillance

and monitoring systems and (c) research to address priorities and

their subsequent translation into practice and policy (Bowen etal.,

2021). Vulnerability, adaptation and capacity assessments include

consideration of the feasibility and effectiveness of priority health

adaptation options and can help decision makers identify strategies

for enhancing adaptation feasibility in speciﬁc contexts.

7.4.2.3 Adaptation Options for Vector-borne, Water-borne and

Food-Borne Diseases

Integrated vector control approaches are crucial to effectively manage

the geographic spread, distribution and transmission of VBDs associated

with climate change (high conﬁdence). Some of the projected risks of

climate change on VBDs can be offset through enhanced commitment

to existing approaches to integrated case management and integrated

vector control management (Cissé etal., 2018; Confalonieri etal., 2017;

Semenza and Paz, 2021). Important components include enhanced

disease surveillance and early warning and response systems that can

identify potential outbreaks at sub-seasonal to decadal time scales

Table7.3 | Summary of adaptation options for key risks associated with climate-sensitive vector-, water- and food-borne diseases (VBDs, WBDs, FBDs).

Key

risk

Geographic

region(s) at

higher risk

Consequence that

would be consid-

ered severe and to

whom

Hazard conditions

that would con-

tribute to this risk

being severe

Exposure condi-

tions that would

contribute to this

risk being severe

Vulnerability con-

ditions that would

contribute to this

risk being severe

Adaptation

options with high

potential for

reducing risk

Selected key

references

VBDs Global

Increase in the

incidence of some

VBDs, such as malaria,

dengue and other

mosquito-borne

diseases, in endemic

areas and in new

risk areas (e.g.,

cities, mountains and

Northern Hemisphere)

Increased climatic

suitability for

transmission (e.g.,

enhanced vectorial

capacity through a

temperature shift)

Large increases in

human exposure to

vectors driven by

growth in human and

vector populations,

globalisation,

population mobility

and urbanisation

Few effective vaccines,

weak health systems,

ineffective personal

and household

protections,

susceptibility to

disease, poverty, poor

hygiene conditions,

insecticide resistance

and behavioural

factors

Improved housing,

better sanitation

conditions and

self-protection

awareness;

insecticide-treated

bed nets and indoor

spraying of insecticide;

broader access to

healthcare for the

most vulnerable;

establishment of

disease surveillance

and early warning

systems for VBDs;

cross-border joint

control of outbreaks;

effective vector

control; targeted

efforts to develop

vaccines

Cissé etal. (2018);

Semenza (2021);

Rocklöv and Dubrow.

(2020)

WBDs

Mostly

low- and

middle-income

countries

(Africa and

Asia); small

islands; global

for Vibrios

Increase in the

occurrence and

intensity of WBDs

such as Vibrios

(particularly V.

cholerae), diarrhoeal

diseases and other

waterborne GI

illnesses

Substantial changes

in temperature and

precipitation patterns,

increased frequency

and intensity of

extreme weather

events (e.g., droughts,

storms and ﬂoods),

ocean warming and

acidiﬁcation

Large increases in

exposure, particularly

in ﬂood-prone

areas with poor

sanitation and

favourable ecological

environments for WBD

pathogens

Poor hygiene

conditions, lack of

clean drinking water

and safe food, ﬂood-

and drought-prone

areas and vulnerable

water and sanitation

systems

Improved WASH

conditions and

surveillance systems;

improved personal

drinking and eating

habits; behaviour

change

Brubacher etal.

(2020); Ford and

Hamner (2018); Lake

(2018); Levy etal.

(2018); Nichols etal.

(2018); Rocklöv etal.

(2021)

FBDs Global

Increase in the

occurrence and

intensity of FBDs

such as Salmonella

and Campylobacter,

including in

high-income countries

Substantial changes

in temperature and

precipitation patterns,

increased frequency

and intensity of

extreme weather

events (e.g., droughts,

storms and ﬂoods),

ocean warming and

acidiﬁcation

Large increases in

exposure, particularly

in ﬂood-prone

areas with poor

sanitation and

favourable ecological

environments for FBD

pathogens

Poor hygiene

conditions; lack

of clean drinking

water and safe

food; ﬂood- and

drought-prone areas;

vulnerable water and

sanitation systems,

food storage systems,

food processes, food

preservation and cold

chain/storage

Improved WASH

conditions and

surveillance systems;

improved personal

drinking and eating

habits; behaviour

change; improved

food storage, food

processing, food

preservation and cold

chain/storage

Brubacher etal.

(2020); Ford and

Hamner (2018); Lake

(2018); Levy etal.

(2018); Nichols etal.

(2018); Rocklöv etal.

(2021)

1108

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

(Rocklöv and Dubrow, 2020; Semenza and Zeller, 2014; Table7.3). In

many cases, the exposure dynamics of VBDs are strongly inﬂuenced by

socioeconomic dynamics that should be considered when developing

and deploying adaptation options (UNEP, 2018). This is especially

the case in low-income countries. For example, insufﬁcient access

to sanitation and the presence of standing water are important

determinants of the presence of Aedes aegypti populations and

the pathogens that cause visceral leishmaniasis (L. donovani and L.

infantum) in urban and peri-urban areas. Low housing quality and lack

of refuse management are associated with higher rodent infestation.

Strategies expected to have important health co-beneﬁts include those

that support health systems strengthening and ecosystem health,

improve access to health coverage, increase awareness and education

and address the underlying conditions of uneven development and a

lack of adequate housing and access to water and sanitation systems

in areas endemic to mosquito-borne diseases (Semenza and Paz, 2021;

Cross-Chapter BoxILLNESS in Chapter 2).

Adaptation options for climate-related risks for WBDs and FBDs

are strongly associated with wider, multi-sectoral initiatives to

improve sustainable development in low-income communities (high

conﬁdence). Effective measures include improving access to potable

water and reducing exposure of water and sanitation systems to

ﬂooding and extreme weather events (Brubacher etal., 2020; Cisse,

2019; Table7.3). This requires focusing on farm-level interventions that

limit the spread of pathogens into adjacent waterways, preventing

the ongoing contamination of water and sanitation systems and the

promotion of food-safe human behaviours (Levy etal., 2018; Nichols

et al., 2018). It is also important to implement well-targeted and

integrated WASH interventions, including at schools and ensuring

proper disposal of excreta and wastewater. Cities can integrate regional

climate projections into their engineering models to produce lower-

risk source waters and increase the resilience of water and sanitation

technologies and management systems under a range of climate

scenarios. Technologies can help abstract source waters from depth,

introduce or increase secondary booster disinfection, design or modify

systems to reduce residence times within pipes and/or coat exposed

pipes (Levy etal., 2018). Other efﬁcient interventions include source

water protection, promoting water ﬁltration, testing the presence of

waterborne pathogens in shellﬁsh, imposing trade restrictions where

necessary and improving hygiene at all levels (Semenza and Paz,

2021). Needed actions include early warning and response systems,

strengthening the resilience of communities and health systems and

promoting ecosystem health, water safety plans and sanitation safety

plans (Brubacher etal., 2020; Cisse, 2019; Ford and Hamner, 2018;

Lake and Barker, 2018; Levy etal., 2018; Nichols etal., 2018; WHO

and International Water Association, 2009; WHO, 2016a; WHO, 2018b;

Semenza, 2021; Rocklöv etal., 2021).

7.4.2.4 Adaptation Options for Heat-Related Morbidity and

Mortality

Adaptations options for heat refer to strategies implemented at short

time scales such as air conditioning and HAPs, including heat warning

systems and longer-term solutions such as urban design and planning

and NbS (Table7.4).

To date, air conditioning is the main adaptation approach for

mitigating the health effects of high temperatures, especially in

relation to cardiorespiratory health (Madureira etal., 2021). However,

air conditioning may constitute a maladaptation because of its high

demands on energy and associated heat emissions, especially in high-

density cities (Eriksen et al., 2021; Magnan et al., 2016; Schipper,

2020), and also lead to ‘heat inequities’ as this is not an affordable or

practical option for many (Jay etal., 2021; Turek-Hankins etal., 2021).

HAPs link weather forecasts with alert and communication systems

and response activities, including public cooling centres, enhanced

heat-related disease surveillance and a range of individual actions

designed to reduce the health effects of extreme heat events such

as seeking shade and altering the pattern of work (McGregor etal.,

2015). While well-designed and operationalisable HAPs possess the

potential to reduce the likelihood of mortality from extreme heat

events (medium conﬁdence) (Benmarhnia et al., 2016; Heo et al.,

2019b; Martinez-Solanas and Basagana, 2019; Martinez etal., 2019;

De’Donato etal., 2018), full process and outcome-based evaluations

of HAPs and their constituent components are lacking (Boeckmann

and Rohn, 2014; Chiabai etal., 2018b; Boeckmann and Rohn, 2014;

Nitschke etal., 2016; Diaz etal., 2019; Benmarhnia etal., 2016; Heo

etal., 2019a; Heo etal., 2019b; Ragettli and Roosli, 2019). Evaluations

of heatwave early warning systems as a component within HAPs

show inconsistent results in terms of their impact on predicting

mortality rates (Nitschke etal., 2016; Benmarhnia etal., 2016; Heo

etal., 2019a; Heo etal., 2019b; Ragettli and Roosli, 2019; Martinez

et al., 2019; De’Donato et al., 2018; Weinberger et al., 2018b),

indicating climate-based heat warning systems, which use a range of

heat stress metrics (Schwingshackl etal., 2021), are not sufﬁcient as a

stand-alone approach to heat risk management (high conﬁdence). To

support HAP and heat risk-related policy development, identiﬁcation

and mapping of heat vulnerability ‘hot spots’ within urban areas have

been proposed (Chen etal., 2019; Hatvani-Kovacs etal., 2018)

A multi-sectoral approach, including the engagement of a range

of stakeholders will likely beneﬁt the response to longer-term heat

risks through the implementation of measures such as climate-

sensitive urban design and planning that mitigates UHI effects (high

conﬁdence) (Ebi, 2019; Jay etal., 2021; Alexander etal., 2016; Levy,

2016; Masson et al., 2018; McEvoy, 2019; Pisello etal., 2018). In

the shorter-term, potentially localised solutions can include awnings,

louvers, directional reﬂective materials, altering roof albedo, mist

sprays, evaporative materials, green roofs and building facades

and cooling centres (Jay et al., 2021; Macintyre and Heaviside,

2019; Spentzou et al., 2021; Takebayashi, 2018). NbS to reduce

heat that offer co-beneﬁts for ecological systems include green and

blue infrastructure (e.g., urban greening/forestry and the creation

of water bodies) (Koc et al., 2018; Lai et al., 2019; Shooshtarian

etal., 2018; Ulpiani, 2019; Zuvela-Aloise et al., 2016; Hobbie and

Grimm, 2020). The implementation of climate-sensitive design and

planning can be constrained by governance issues (Jim etal., 2018)

and the beneﬁts are not always evenly distributed among residents.

Implementation of climate-sensitive design and NbS does, however,

need to be carried out within the context of wider public health

planning because water bodies and moist vegetated surfaces provide

suitable habitats for a range of disease vectors (Nasir etal., 2017;

Tian et al., 2016; Trewin etal., 2020). Solutions recommended for

1109

Health, Wellbeing and the Changing Structure of Communities Chapter 7

managing exposure to heat in outdoor workers include improved

basic protection (including shade and planned rest breaks), heat-

appropriate personal protective equipment, work scheduling for

cooler times of the day, heat acclimation, improved aerobic ﬁtness,

access to sufﬁcient cold drinking water and on-site cooling facilities

and mechanisation of work (Morabito et al., 2021; Morris et al.,

2020; Varghese etal., 2020; Williams etal., 2020).

Most adaptation options were developed in high- and middle-income

countries and typically require signiﬁcant ﬁnancial resources for their

planning and implementation. Studies are needed of the beneﬁts of

indigenous and non-Western approaches to managing and adapting

to extreme heat risk. Recently published reviews of approaches

to heat adaptation outline the nature and limitations of a range of

cooling strategies with optimal solutions for a number of settings

recommended (Jay etal., 2021; Turek-Hankins etal., 2021).

7.4.2.5 Adaptation Options for Air Pollution-related Health

Effects

As noted in Section 7.3.1.6, air pollution projections indicate

ambitious emission reduction scenarios or stabilisation of global

temperature change at 2°C or below would yield substantial co-

beneﬁts for air quality-related health outcomes. Improvements in

air quality could be achieved by the deliberate adoption of a range

of adaptation options to complement mitigation measures such

as decarbonisation (e.g., renewable energy, fuel switching, energy

efﬁciency gains and carbon capture storage and utilisation) and

negative emissions technologies (e.g., bioenergy carbon capture and

storage, soil carbon sequestration, afforestation and reforestation

and wetland construction and restoration).

Adaptation options for air pollution include implementing ozone

precursor emission control programmes; developing mass transit/

efﬁcient public transport systems in large cities; encouraging car-

pooling, cycling and walking (active transport); trafﬁc congestion

charges; low emission zones in cities; integrated urban planning

implementing NbS such as green infrastructure for pollutant

interception and removal; managing wildﬁre risk regionally and

across jurisdictional boundaries; developing air quality warning

systems; altering activity on high pollution days; effective air

pollution risk communication and education; wearing protective

equipment such as face masks; avoiding solid fuels for cooking and

indoor heating; ventilating and isolating cooking areas; and using

portable air cleaners ﬁtted with high-efﬁciency particulate air ﬁlters

(Abhijith etal., 2017; Carlsten etal., 2020; Cromar etal., 2020; Ding

Table7.4 | Summary of adaptation options for key health risks associated with heat.

Key risk

Geographic

region

Consequence

that would

be considered

severe, and to

whom

Hazard condi-

tions that would

contribute to

this risk being

severe

Exposure condi-

tions that would

contribute to

this risk being

severe

Vulnerability

conditions that

would contrib-

ute to this risk

being severe

Adaptation options

with high potential

for reducing risk

Selected key

references

Heat-related

mortality,

morbidity and

mental illness

– Global but

especially where

temperature

extremes

beyond physical

and mental

health and

thermal comfort

threshold levels

are expected to

increase

– Substantial

increase in

heat-related

mortality and

morbidity rates,

especially in

urban centres

(heat island

effect) and rural

areas (outside

workers),

outdoors in

general (sports

and related

activities) and

for people

suffering from

obesity, weak

cardiovascular

capacity /physical

ﬁtness

– Increased risk

of respiratory

disease and CVD

mortality

– Loss of economic

productivity

– Substantial

increase in

mental illness

compared to

base rate

– Substantial

increase in

frequency and

duration of

extreme heat

events, especially

in cities where

heat will be

exacerbated by

UHI effects

– Unintended

increases

in urban

temperatures

from

anthropogenic

heat (vehicles,

air conditioning,

urban

metabolism)

– Increased number

of days with high

temperatures

in non-urban

settings such as

agricultural areas

– Large increases

in urban heat and

population heat

exposure driven

by demographic

change (e.g.,

aging) and

increasing

urbanisation

– Exposure will

increase amongst

agricultural and

construction

workers

– Mortality/

morbidity:

Increases in

the number of

very young and

elderly and of

those with other

health conditions

such as lack of

physical ﬁtness,

obesity, diabetes

and associated

comorbidities;

lack of

adaptation

capacity

– Mental illness:

Lack of air

conditioning;

lack of access

to healthcare

systems and

services

– Heat warning systems.

– Improved building

and urban design

(including green and

blue infrastructure)

and passive

cooling systems,

acknowledging

that not all will

have access to air

conditioning

– Broader

understanding of heat

hazard and better

access to public health

systems for the most

vulnerable

– Application where

possible of renewable

energy sources

– Communication

around drinking

water; availability

of clean water via

simple effective water

puriﬁcation systems

in low water quality

settings; water spray

cooling

– Mental health support

Benmarhnia etal.

(2016); Chen

etal. (2019); Jay

etal. (2021); Heo

etal. (2019b);

Martinez-Solanas

and Basagana

(2019); Morabito

etal. (2021);

Schwingshackl

etal. (2021)

1110

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Table7.5 | Summary of adaptation options for key health risks associated with air pollution.

Key risk

Geographic

region

Consequence

that would

be considered

severe and to

whom

Hazard conditions

that would con-

tribute to this risk

being severe

Exposure condi-

tions that would

contribute to

this risk being

severe

Vulnerability

conditions that

would contrib-

ute to this risk

being severe

Adaptation op-

tions with high

potential for

reducing risk

Selected key

references

Air

pollution-related

health effects

– Global, but

especially in

regions with

existing poor

air quality,

particularly in

relation to PM

and ozone

– Greatest climate

change driven

ozone-related

mortality is

expected for East

Asia and North

America

– For PM the

highest

climate and air

quality-related

mortalities are

projected for

India, the Middle

East, Former

Soviet Union and

East Asia

– Substantial

increase in air

pollution-related

mortality and

morbidity rates,

especially in

urban centres,

related to both

severe pollution

episodes and

longer-term

deterioration of

air quality

– People

particularly

vulnerable

include those

with RTIs and

CVD

– Increase in

mental illness

(depression) as

a result of poor

air quality and

visibility

– Non-achievement

of emission

reduction targets

– Substantial increase

in frequency

and duration of

meteorological

conditions

conducive to

the buildup of

both primary

and secondary

air pollutants

(e.g., greater

frequency of

calm atmospheric

‘blocking’

conditions) and

no long-term

improvement in air

quality at a range

of geographical

scales (global to

local)

– Increase in

frequency and

intensity of

wildﬁres and dust

storms

– Increase in the

intensity of UHIs,

especially in the

summer, and the

occurrence of ozone

episodes due to

anomalously high

urban temperatures

– Large increases

in exposure to air

pollutants driven

by demographic

change (e.g.,

aging) and

increasing

urbanisation

– For arid regions

increases in

exposure to dust

storms

– Areas adjacent/

downwind of

major wildﬁres

– For urban

populations

intensifying UHIs

and enhanced

formation of

secondary

pollutants

– Increases in

the number of

very young and

elderly and those

with respiratory

or cardiovascular

conditions,

and lack of

adaptation

capacity (e.g.,

reduced reliance

on solid fuel for

cooking/heating)

– Mental illness:

Lack of access

to healthcare

systems and

services

– Air quality

management

policies, air

quality warning

systems, efﬁcient

and cheap mass

transit systems,

integrated

urban planning

(including NbS

and green

infrastructure)

– Broader

understanding

of air pollution

hazard and better

access to public

health systems

for the most

vulnerable

– Application

where possible of

renewable energy

sources to reduce

emissions

Carlsten etal.

(2020); Doherty

etal. (2017);

Jennings etal.

(2021); Kumar etal.

(2019); Orru etal.

(2017); Orru etal.

(2019); Schumacher

and Shandas

(2019); Silva etal.

(2017); Voordeckers

etal. (2021)

etal., 2021; Holman etal., 2015; Jennings etal., 2021; Kelly etal.,

2021; Kumar et al., 2019; Masselot et al., 2019; Ng et al., 2021;

Riley, 2021; Voordeckers et al., 2021; Xu et al., 2017; Table 7.5).

While the range of air pollution adaptation options is potentially

extensive, barriers may need to be overcome to achieve successful

implementation, including ﬁnancial, institutional, political (i.e. inter-

and intra-governmental) and social barriers (Barnes et al., 2014;

Ekstrom and Bedsworth, 2018; Fogg-Rogers etal., 2021; Schumacher

and Shandas, 2019).

7.4.2.6 Multi-sectoral Adaptation for Risks of Malnutrition

Adaptation to reduce the risk of malnutrition requires multi-sectoral,

integrated approaches (very high conﬁdence). Adaptation actions

include access to healthy, affordable diverse diets from sustainable

food systems (high conﬁdence); a combination of access to health—

including maternal, child and reproductive health— and nutrition

services, water and sanitation (high conﬁdence); access to nutrition-

sensitive and shock-responsive social protection (high conﬁdence);

and early warning systems (high agreement), risk sharing, transfer,

and risk reduction schemes such as index-based weather insurance

(medium conﬁdence) (Mbow et al., 2019; Swinburn et al., 2019;

UNICEF/WHO/WBG, 2019; FAO et al., 2021; Macdiarmid and

Whybrow, 2019; Liverpool-Tasie etal., 2021). Common enablers across

adaptation actions that enhance the effectiveness and feasibility of

the adaptation include: education, women’s and girls’ empowerment

(high conﬁdence), rights-based governance and peacebuilding social

cohesion initiatives such as the framework of the Humanitarian

Development and Peace Nexus (medium conﬁdence).

Nutrition-sensitive and integrated agroecological farming systems

offer opportunities to increase dietary diversity at household levels

while building local resilience to climate-related food insecurity

(high conﬁdence) (Bezner Kerr et al., 2021; IPES-Food, 2020; Altieri

et al., 2015) especially when gender equity, racial equity and

social justice are integrated (Bezner Kerr et al., 2021). Adaptation

responses include a combination of healthy, culturally appropriate

and sustainable food systems and diets; soil and water conservation;

1111

Health, Wellbeing and the Changing Structure of Communities Chapter 7

social protection schemes and safety nets; access to health services;

nutrition-sensitive risk reduction; community-based development;

women’s empowerment; nutrition-smart investments; increased policy

coherence; and institutional and cross-sectoral collaboration (high

agreement, medium evidence) (FAO etal., 2018; Mbow etal., 2019;

Pozza and Field, 2020; FAO etal., 2021; Table7.7). Nutrition security can

be enhanced through consideration of nutrient ﬂows in food systems

(Harder et al., 2021).This ‘circular nutrient economy’ perspective

highlights the potential for adaptations throughout the food supply

chain, including sustainable production practices that promote

nutrient diversity and density, processing, storage, and distribution that

conserves nutrition; equitable access and consumption of available,

affordable, appropriate, and healthy foods; and waste management

that supports nutrient recovery (Harder etal., 2021; Boon and Anuga,

2020; FAO etal., 2021; Pozza and Field, 2020; Ritchie etal., 2018).

Traditional, indigenous and small-scale agroecology and regional food

systems provide context-speciﬁc adaptations that promote food and

nutrition security as well as principles of food sovereignty and food

systems resilience (HLPE, 2020; Bezner Kerr etal., 2021; IPES-Food,

2020; IPES-Food, 2018).

Table7.6 | Feasibility and effectiveness assessments of multi-sectoral adaptation for food security and nutrition.

Feasibility and effectiveness assessments

of multisectoral adaptation for food security and nutrition

Climate change impacts on food security and nutrition

Key risk: Malnutrition in all its forms linked to decline in food availability and increased

cost of healthy food

Adaptation option

Assessment results

Feasibility dimensions

Climate-resilient, nutrition-sensitive and agroecological food production

Sustainable and healthy diets (local, equitable, diverse)

Access to health, nutrition services and healthy environments (water and sanitation)

Early warning systems to prevent adverse effects on nutrition

Nutrition-sensitive social protection

Nutrition-sensitive risk reduction, risk sharing and insurance

Technological

Institutional

Geophysical

Social

Environmental

Agreement

Economic

Evidence

Effectiveness

Enabler relevance

Rights-based approach

and good governance

Humanitarian Development

and Peace nexus

Education

Women empowerment

= no data or not assessedna

HighMediumLow

Table7.7 | Summary of adaptation options for key risks associated with malnutrition.

Key risk

Geographic

region

Consequence that

would be consid-

ered severe and to

whom

Hazard condi-

tions that would

contribute to

this risk being

severe

Exposure condi-

tions that would

contribute to

this risk being

severe

Vulnerability

conditions that

would contrib-

ute to this risk

being severe

Adaptation options

with high potential

for reducing risk

Selected key

references

Malnutrition

due to

decline

in food

availability

and

increased

cost of

healthy food

– Global, with

greater risks in

Africa, south

Asia, Southeast

Asia, Latin

America, the

Caribbean and

Oceania

– Substantial number

of additional

people at risk of

hunger, stunting,

and diet-related

morbidity and

mortality, including

decreased mental

health and cognitive

function

– Micro- and

macronutrient

deﬁciencies

– Severe impacts

on low-income

populations from

LIMICs

– Risks especially

high for groups

that suffer greater

inequality and

marginalisation

– Climate changes

leading to

reductions in

crop, livestock

or ﬁsheries

yields, including

temperature and

precipitation

changes and

extremes,

drought, and

ocean warming

and acidiﬁcation

– Large numbers of

people in areas

and markets

particularly

affected by

climate impacts

on food security

and nutrition

– High levels

of inequality

(including gender

inequality) and

substantial

numbers of

people subject

to poverty or

violent conﬂict,

in marginalised

groups or with

low education

levels

– Slow economic

development.

– Ineffective

social protection

systems, nutrition

services, and

health services

– Multi-sectoral

approach to

nutrition-sensitive

adaptation

and disaster

risk reduction/

management,

including food, health

and social protection

systems

– Inclusive governance

involving

marginalised groups

– Improved education

for girls and women

– Maternal and child

health, water and

sanitation, gender

equality, climate

services and

social protection

mechanisms

Glover and Poole

(2019); Mbow

etal. (2019);

Swinburn etal.

(2019)

1112

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

A feasibility and effectiveness assessment was conducted for six

adaptation strategies often used and recommended by the UN to

respond to malnutrition risks that combined a literature review and

expert judgment assessment of 80 peer-reviewed studies (UNSCN,

2010; Tirado et al. 2013; methods adapted from de Coninck et al.

(2018) and Singh etal. (2020)). Nineteen indicators of six dimensions

of feasibility (economic, technical, social, institutional, environmental

and geophysical) were considered. The lead time to initiate and the

expected longevity of each option were examined. Feasibility was

deﬁned as how signiﬁcant the reported barriers were to implement a

particular adaptation option. Highly feasible options were those where

no or very few barriers were reported. Moderately feasible were those

where barriers existed but did not have a strong negative effect on

the adaptation option (or evidence was mixed). Low feasibility options

had multiple barriers reported that could block implementation.

Effectiveness ratings were based on expert consultation and reﬂected

the potential of the adaptation option to reduce risk. The ﬁnal

effectiveness and feasibility scores were categorised as high, medium

or low and reﬂect the combined results of all studies for a given

adaptation option (Table7.6).

Adaptive social protection programmes and mechanisms that can

support food insecure households and individuals include cash transfers

or public work programmes, land reforms, and extension of credit and

insurance services that reduce food insecurity and malnutrition during

times of environmental stress (Carter and Janzen, 2018; Johnson

et al., 2013; Alderman, 2016). For example, children from families

participating in Ethiopia’s Productive Safety Net Program experienced

improved nutritional outcomes, partly due to better household food

consumption patterns and reduced child labour (Porter and Goyal,

2016). School feeding programmes improve nutritional outcomes,

especially among girls, by promoting education, and by reducing child

pregnancy and fertility rates (Bukvic and Owen, 2017). Adaptive social

protection is most effective when it combines climate risk assessment

with DRR and wider socioeconomic development objectives (Davies

etal., 2013).

Transformative approaches towards healthier, more sustainable,

plant-based diets require integrated strategies, policies and

measures, including economic incentives for the agroecological

production and equitable access to and consumption of more

fruits, vegetables and pulses; inclusion of sustainability criteria in

dietary guidelines, labelling and public education programmes; and

promoting collaboration, good governance and policy coherence

(Glover, 2019).

7.4.2.7 Adaptation Options for Risks to Mental Health

Adaptation options for reducing mental health risks associated with

extreme weather include preventive and post-event responses (high

conﬁdence) (Brown et al., 2017; Cohen, 2019; James et al., 2020;

Table7.8). Responses include improving funding and access to mental

healthcare, which is under-resourced (WHO, 2019a); surveillance

and monitoring of psychosocial impacts of extreme weather events;

community-level planning for mental health as part of climate-

resilience planning (Clayton et al., 2017); and mental health and

psychological ﬁrst aid training for care providers and ﬁrst responders

(Hayes etal., 2018; O’Donnell etal., 2021; Hayes etal., 2018; Taylor,

2020; Morgan etal., 2018; Sijbrandij etal., 2020). Legislation can

ensure access to services as well as establish a regulatory framework

(Ayano, 2018). Advanced disaster risk planning reduces post-event

mental health challenges. One example is from China, where pre-

Table7.8 | Summary of adaptation options for key risks associated with mental health.

Key Risk

Geographic

region

Consequence

that would

be considered

severe and to

whom

Hazard condi-

tions that would

contribute to

this risk being

severe

Exposure condi-

tions that would

contribute to

this risk being

severe

Vulnerability

conditions that

would contrib-

ute to this risk

being severe

Adaptation options

with high potential

for reducing risk

Selected key

references

Mental health

impacts in

response to

ﬂoods, storms,

and wildﬁres

– Global; some

areas at greater

risk for storms,

ﬂooding, or

wildﬁres

– Substantial

increase in

mental illness

compared to

base rate

– Increased

frequency of

major storms,

weather-related

ﬂooding or

wildﬁres

– Low-lying areas,

dry areas, urban

areas

– Physical

infrastructure

that is vulnerable

to extreme

weather,

inadequate

emergency

response and

mental health

services, social

inequality

– Improved urban

infrastructure,

warning systems, and

post-disaster social

support

– Improved funding

and access to mental

healthcare

– Improved surveillance

and monitoring of

mental health impacts

of extreme weather

events

– Climate change

resilience planning

in the mental health

system (including at a

community level

– Mental health ﬁrst

aid training for care

providers and ﬁrst

responders

Ali etal. (2020);

Ayano (2018);

Buckley etal.

(2019); Clayton

etal. (2017);

Hayes etal. (2019);

James etal. (2020);

Sijbrandij etal.

(2020)

1113

Health, Wellbeing and the Changing Structure of Communities Chapter 7

planning of temporary shelters resulted in signiﬁcantly lower rates

of anxiety, depression and PTSD in the aftermath of ﬂooding among

displaced people who accessed them (Zhong et al., 2020). Key

elements of successful initiatives include coordinated planning and

action between key regional agencies and governments with a focus

on improving accountability and removing barriers to implementation

and subsequent access to programmes (Ali et al., 2020). As an

example, following the 2019/2020 Australian bushﬁres, the federal

government allocated funds to support mental health through

free counselling for those affected, increased access to telehealth,

extended hours for mental health services and programmes designed

speciﬁcally for youth (Newnham etal., 2020).

Because mental health is fundamentally inter-twined with social and

economic well-being, adaptation for climate-related mental health

risks beneﬁts from wider multi-sectoral initiatives to enhance well-

being, with the potential for co-beneﬁts to emerge (high conﬁdence).

Improvements in education, quality of housing, safety and social

protection support enhance general well-being and make individuals

more resilient to climate risks (Lund etal., 2018; Hayes etal., 2019).

Among Indigenous Peoples, connections to traditional culture and

to place are associated with health and well-being (Bourke et al.,

2018) as well as with resilience to environmental change (Ford etal.,

2020). As an example of the connection between infrastructure

improvements and mental health, a study of domestic rainwater

harvesting initiatives to promote household water security also

improved mental health in participating households (Mercer and

Hanrahan, 2017). Adaptive urban design that provides access to

healthy natural spaces—an option for reducing risks associated with

heat stress—also promotes social cohesion and mitigates mental

health challenges (high conﬁdence) (Buckley et al., 2019; Clayton

etal., 2017; Jennings and Bamkole, 2019; Liu etal., 2020b; Mygind

etal., 2019; Marselle etal., 2020).

7.4.2.8 Adaptation Options to Facilitate Early-Warning and

Response Systems

Early warning systems are a potentially valuable tool in adapting to

climate-related risks associated with infectious diseases when based

on forecasts with high skill and when there are effective responses

within the time frame of the forecast (high conﬁdence). Through

advanced seasonal weather forecasting that draws upon established

associations between weather/climate and infection/transmission

conditions, conditions conducive to disease outbreaks can be identiﬁed

months in advance, providing time to implement effective population

health responses (Morin et al., 2018). Most current early warning

systems are focused on malaria and dengue but there are examples

for other diseases, such as an early warning system developed

for Vibrios monitoring in the Baltic Sea (Semenza etal., 2017). An

early warning system for dengue outbreaks in Colombia based on

temperature, precipitation and humidity successfully detected 75%

of all outbreaks between one and ﬁve months in advance, detecting

12.5% in the same month (Lee et al., 2017b). Dengue warning

systems in Brazil, Malaysia and Mexico have generated satisfactory

results (Hussain-Alkhateeb etal., 2018). An effective early warning

system for malaria was implemented in the Amhara region of Ethiopia

(Merkord etal., 2017).

Early warning systems are effective at detecting and potentially

reducing food security and nutrition risks (high conﬁdence). Examples

of proven systems include the United States Agency for International

Development (USAID) Famine Early Warning System, the Food and

Agricultural Organization’s Global Information and Early Warning

System and the World Food Programme’s Corporate Alert System. Such

systems are fundamental for anticipating when a crisis might occur

and setting priorities for interventions (Funk etal., 2019). Financial

investments to develop early warning systems are cost-effective and

reduce human suffering (Choularton and Krishnamurthy, 2019) (high

conﬁdence). For instance, during the 2017 drought-induced food crisis

in Kenya, 500,000 fewer people required humanitarian assistance

than would have been expected based on past experiences; this was

largely due to timely and effective interventions triggered by the early

warning (Funk etal., 2018).

Early warning systems have been established for other climate-

sensitive health outcomes, such as respiratory diseases associated

with air pollution (Shih et al., 2019; Li and Zhu, 2018; Yang and

Wang, 2017). Early warning systems for non-heat extreme weather

and climate events, such as storms and ﬂoods, are designed to

protect human health and well-being; disaster risk management

organisations and institutions typically communicate these warnings

through their networks. Research is ongoing to extend the time

period for warnings.

7.4.2.9 Incorporating Disaster Risk Reduction into Health

Adaptation

Integrating health into national disaster risk management plans has

wider beneﬁts for resilience and adaptation to climate change risks

(high conﬁdence) (UNFCCC, 2017a; Watts etal., 2019). DRR, including

disaster preparedness, management and response, is widely recognised

as important for reducing health consequences of climate-related

hazards and extreme weather events (Keim, 2008; Phalkey and Louis,

2016). A systematic review by Islam etal. (2020) identiﬁed multiple,

ongoing challenges to integrating climate adaptation and DRR at

global and national levels, including a lack of capacity among key

actors and institutions, a lack of coordination and collaboration across

scales of government and general lack of funding—challenges that

are particularly relevant for the health sector. Global events, including

climate-related extreme events and public health emergencies of

international concern (for example, Ebola, Middle East respiratory

syndrome (MERS) and COVID-19) have inﬂuenced the development

of national public health preparedness and response systems and

attracted signiﬁcant investment over the last two decades (Khan etal.,

2015; Murthy etal., 2017; Watson etal., 2017). The Sendai Framework

for Disaster Risk Reduction and the International Health Regulations

establish important global and regional goals for increasing health

system resilience and reducing health impacts from biological hazards

and extreme climate events (Aitsi-Selmi etal., 2015; Maini etal., 2017;

UNFCCC, 2017b; Wright etal., 2020). There are explicit links between

the health aspect of the Sendai Framework and UN SDGs 1, 2, 3, 4, 6, 9,

11, 13, 14, 15 and 17 (Wright etal., 2020). More speciﬁcally, reducing

the number of disaster-related deaths, illnesses and injuries, as well as

damage to health facilities are key indicators for achieving the goals

set out in the Sendai Framework (UNFCCC, 2017b).

1114

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

The intersection of health and multi-sectoral DRR and manage-

ment, generally described as health emergency and disaster risk

management (health-EDRM), encompasses multi-sectoral approaches

from epidemic preparedness and response including the capacities

for implementing the International Health Regulations (IHR, 2005),

health systems strengthening and health systems resilience (Lo Iacono

et al., 2017; WHO 2019; Wright et al., 2020). Health-EDRM costs to

governments are notably lower than the cost of inaction (Peters etal.,

2019). Additional per capita costs in low-income countries have been

estimated to range from USD 4.33 (capital) and USD 4.16 (annual

recurrent costs), and in upper middle-income countries to an additional

USD 1.35 in capital costs and USD 1.41 in extra annual recurrent costs

(Peters et al., 2019). Adopting a health-EDRM approach supports the

systematic integration of health and multi-sectoral EDRM to ensure a

holistic approach to health risks and assists in the alignment of action in

health security, climate change and sustainable development (Chan and

Peijun, 2017; Dar etal., 2014; WHO, 2019; Wright etal., 2020).

Climate-informed health-EDRM is crucial for the climate resilience of

health systems (WHO, 2015a), particularly to account for additional

risks and uncertainties associated with climate change and allow for

well-planned, effective and appropriate EDRM and adaptation (Watts

etal., 2018a; WHO, 2013; WHO, 2015a). Potential coherent approaches

to addressing climate change and disaster risks to health include:

strengthening health systems; vulnerability and risk assessments that

incorporate disaster and climate change risk; building resilience of

health systems and health infrastructure; and climate-informed EWSs

(Banwell etal., 2018; Phalkey and Louis, 2016). However, a review of

DRR projects including climate change in south Asia found that the

health sector was the least represented with only 2% of 371 projects

relating to health (Mall etal., 2019), indicating a need to strengthen

the incorporation of climate change in health-EDRM. Current tracking

under the Sendai Framework of Disaster Risk Reduction 2015–2030

shows that most countries (particularly low-income countries and

lower middle-income countries) still lack robust systems for integrated

risk monitoring and early warning (UNEP, 2018). The incorporation of

DRR and management strategies into climate adaptation for health

and health systems at local scales is particularly important, given that

it is at local scales where health services are most often delivered

and where knowledge of speciﬁc needs and challenges is often

greatest (Amaratunga etal., 2018; Schramm etal., 2020a). Indigenous

knowledge has been shown to be valuable in DRR, with particularly

strong evidence existing for drought risk reduction in sub-Saharan

Africa (Fummi etal., 2017; Muyambo etal., 2017; Dube and Munsaka,

2018; Macnight Ngwese et al., 2018). In the USA, DRR strategies

that draw upon traditional knowledge and local expertise are being

incorporated into climate adaptation planning for health in a number

of indigenous communities under the ‘Climate-ready Tribes Initiative’

(Schramm etal., 2020b).

7.4.2.10 Monitoring, Evaluation and Learning

Monitoring, evaluation and learning (MEL) can assess the ability of

nations and communities to prepare for and adequately respond to the

health risks of climate change over time (high conﬁdence) (Boyer etal.,

2020). MEL describes a process that includes baseline assessment,

prioritising actions and activities, identifying key indicators to track,

ongoing data collection and periodically considering new information

(Kruk et al., 2015). MEL determines whether adaptation options

achieved their goals and whether resources were used effectively and

efﬁciently (Boyer etal., 2020). One of the challenges for MEL in the

context of adaptation is that climate risks vary as a function of time,

location, socioeconomic development, demographics and activities in

other sectors (Ebi etal., 2018a). MEL indicators in the health sector

need to account for factors related to governance, implementation

and learning as well as for exposures, impacts and programmatic

activities, all of which are context dependent and are often outside

the health sector (Boyer etal., 2020; Ebi etal., 2018a; Fox etal., 2019).

No universal standardised approach exists for monitoring or evaluating

adaptation activities in the health sector (high conﬁdence). Candidate

indicators of climate change health impacts and adaptation activity,

typically at the national level, are available (Bowen and Ebi, 2017;

Cheng and Berry, 2013; Kenney etal., 2016; Navi etal., 2017; WHO,

2015b). Indicators are best grouped by category of activity, that is,

vulnerability, risk and exposure; impacts; and adaptation and resilience

(Ebi etal., 2018a). As health adaptation expands, enhanced monitoring

will be needed to ensure that scientiﬁc advances are translated into

policy and practice. A promising initiative that emerged since the AR5

is the Lancet Countdown, which represents a global effort at tracking

various indicators of exposures, impacts, adaptation activities, ﬁnance

and media activity related to climate change and health (Watts etal.,

2018a), although this effort is principally focused on monitoring and

does not explicitly focus on evaluation adaptation efforts or learning

from adaptation efforts.

Community-based monitoring of adaptation responses to health

impacts, especially by Indigenous Peoples, has not been widely

undertaken, despite its potential to improve monitoring of and local

adaptation to environmental change (Kipp etal., 2019). The health

sector has been particularly weak at recognising the climate impacts

on and the adaptation needs of Indigenous Peoples and in engaging

Indigenous Peoples in monitoring progress (Ford etal., 2018; David-

Chavez and Gavin, 2018; Ramos-Castillo et al., 2017). Successful

adaptation to the health impacts of climate change in Indigenous

Peoples requires recognition of their rights to self-determination,

focusing on indigenous conceptualisations of well-being, prioritising

Indigenous knowledge and understanding the broader agenda of

decolonisation, health and human rights (high conﬁdence) (Ford and

King, 2015; Green and Minchin, 2014; Hoy etal., 2014; Jones, 2019;

Jones etal., 2014; Mugambiwa, 2018; Nursey-Bray and Palmer, 2018).

Indicators should capture measures of processes that drive adaptation

readiness, including leadership, institutional learning and inter-

sectoral collaboration (Boyer et al., 2020; Ford and King, 2015) as

well as outcome measures such as the presence of programming

known to reduce risks (Ebi et al., 2018a). Additionally, indicators

related to scaling up of effective interventions and relying on the

implementation of science frameworks are important (Damschroder

etal., 2009; Theobald etal., 2018, 2020; Ebi etal., 2018a; Fox etal.,

2019). Measuring impacts attributable to climate change could be

addressed with a combination of indicators related to overall health

system performance and population vulnerability (Ebi etal., 2017; Ebi

etal., 2018a).

1115

Health, Wellbeing and the Changing Structure of Communities Chapter 7

7.4.3 Enabling Conditions and Constraints for Health

Adaptation

7.4.3.1 Governance, Collaboration and Coordination

Effective governance institutions, arrangements, funding and mandates

are key for adaptation to climate-related health risks (high conﬁdence).

Without integration and collaboration across sectors, health adaptation

can become siloed, leading to less effective adaptation or even

maladaptation (Magnan etal., 2016; Fox etal., 2019). Integration and

collaboration include working laterally across national government

departments and agencies, as well as vertically from national

agencies to local governments and with the private sector, academia,

NGOs and civil society. In this context, top-down policy design and

implementation are complemented by bottom-up approaches that

engage community actors in programme design and draw upon their

local practices, perspectives, opinions and experiences. Opportunities

exist to better integrate public health into climate change discourse and

policymaking processes, and to strengthen public health partnerships

and collaborations (Awuor etal., 2020). Creating networks, integration

across organisations and jointly developed policies can facilitate cross-

sectoral collaboration (Bowen and Ebi, 2017).

7.4.3.2 Multi-sectoral Collaborations

Multi-sectoral collaborations aimed at strengthening the health sector

can generate multiple co-beneﬁts in other sectors (high agreement,

medium evidence). Solutions for the health and well-being risks

described in 7.2 and 7.3 often have their origins in sectors that include

water, sanitation, agriculture, food systems, social protection systems,

energy and key components of urban systems such as housing and

employment (WHO, 2015a; Bowen et al., 2014b; Machalaba et al.,

2015; Confalonieri etal., 2015; Bowen etal., 2014a; Semenza, 2021).

Climate resilient development pursued in these other sectors, and

in cooperation with the health sector, simultaneously increases the

potential for adaptation and climate resilience in terms of health and

well-being (high conﬁdence) (Ahmad etal., 2017; Watts etal., 2018b;

Levy and Patz, 2015; WHO, 2018a; Chiabai etal., 2018a; Dudley etal.,

2015; Zinsstag etal., 2018; Sherpa etal., 2014).

7.4.3.3 Financial Constraints

Financial constraints are the most referenced barrier to health

adaptation and so scaling up ﬁnancial investments remains a key

international priority (very high conﬁdence) (Wheeler and Watts, 2018;

UNFCCC, 2017a). AR5 estimated the costs of adaptation in developing

countries at between USD70billion and USD100billion annually in

the year 2050, but these are likely to be a signiﬁcant underestimate,

particularly in the years 2030 and beyond (UNEP, 2014). National surveys

conducted by WHO identiﬁed ﬁnancial constraints as a major barrier to

the implementation of health adaptation priorities (WHO, 2019b; Watts

etal., 2021). Novel research drawing on global ﬁnancial transaction data

suggests that in 2019, global ﬁnancial transactions with the potential

to deliver adaptation in the health and healthcare sector reached

USD 18.4 billion, driven by transactions in high- and upper middle-

income countries, with investment in Africa, Southeast Asia and the

eastern Mediterranean mostly stagnant (Watts etal., 2021).

There has been limited participation of the health sector in

international climate ﬁnancing mechanisms (Martinez and Berry,

2018). Of 149 projects listed in the Adaptation Fund database in

October 2020, a large number were broad-based initiatives that

may have considerable indirect beneﬁts for health systems, such as

enhanced disaster preparedness and food security, but none were

explicitly aimed at strengthening health systems or had directed funds

through ministries of health. A review of projects funded by the major

multi-lateral climate funds showed that less than 1.5% of dispersed

adaptation funding and less than 0.5% of overall funding have been

allocated to projects aimed at protecting health (WHO, 2015a). A

survey of national public health organisation representatives from

a mix of low-, middle- and high-income countries found that a lack

of political commitment, insufﬁcient coordination across sectors and

inadequate funding for public health-speciﬁc adaptation initiatives

were common barriers to building climate resilience (Marcus and

Hanna, 2020). Under-investment in climate-speciﬁc initiatives in health

systems coincides with persistent under-investment in healthcare more

generally, especially in low- and middle-income countries (Schaferhoff

etal., 2019).

Adaptation ﬁnancing does not often reach places where the

climate-sensitivity of the health sector is greatest (Weiler, 2019).

Financial constraints in Africa are one of the key reasons for slow

implementation of health adaptation measures (Nhamo and Muchuru,

2019). Strengthening health systems in vulnerable countries has the

potential to reduce current and future economic costs related to

environmental health risks, thus enabling reinvestment in the health

system and sustainable development (WHO, 2020a; WHO, 2015a).

Robust and comprehensive climate and health ﬁnancing builds ﬁrst

on core health sector investments (WHO, 2015a). Other potential

opportunities for resource mobilisation include health-speciﬁc funding

mechanisms, climate change funding streams and investments from

multi-sectoral actions and actions in health-determining sectors (WHO,

2015a). Incorporating climate change and health considerations into

disaster reduction and management strategies could improve funding

opportunities and increase potential funding streams (Aitsi-Selmi

et al., 2015). Reinforcing cross-sectoral governance mechanisms

maximises health co-beneﬁts and economic savings by allowing for

multi-sectoral costs and beneﬁts to be comprehensively considered

in decision-making (Belesova etal., 2016; WHO, 2020a; WHO, 2015b).

An additional ﬁnancial need concerns health research, the existing

funding for which does not match what is needed to support the

implementation of the combined objectives of the UN 2030 Agenda

for Sustainable Development, the Sendai Framework for Disaster Risk

Reduction and the Paris Agreement (Green and Minchin, 2014; Ebi,

2016; Green etal., 2017).

7.4.3.4 Perceptions of Climate Change Risks and Links to

Adaptation

Adaptation decisions and responses to climate change can be

inﬂuenced by perceptions of risks, which are shaped by individuals’

characteristics, knowledge and experience (medium agreement,

medium evidence). Institutional and governmental responses are

critical for adapting to climate-related risks in health and other

sectors, but individual responses also are relevant, such as choosing

1116

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

to implement adaptation measures. Individual responses are in turn

affected not only by capabilities but also by perceptions that climate

change is real and requires a response (Ogunbode et al., 2019).

Perceptions of climate risks are formed by experiences of changes in

local weather and extreme weather events (Sattler etal., 2018; Sattler

et al., 2020; van der Linden, 2015), observations of environmental

changes (Hornsey etal., 2016), experiences of and knowledge about

climate change impacts (Ngo etal., 2020; van der Linden, 2015) and

individual characteristics such as values and worldviews (Poortinga

et al., 2019) (high agreement, medium evidence). Risk perceptions

include both logical assessments about the likelihood and severity of

climate change impacts and affective feelings about those impacts.

On average, affective measures of risk perception are more strongly

associated with disaster preparation than cognitive measures

(Bamberg etal., 2017; van Valkengoed and Steg, 2019).

In addition to perceptions of risk, the likelihood that an individual will

implement behavioural adaptations or support relevant public policy is

affected by subjective assessments of the response options (Bamberg

et al., 2017; van Valkengoed and Steg, 2019; Akompab et al., 2013;

Carman and Zint, 2020; Hornsey etal., 2016; Brenkert-Smith etal., 2015).

Efﬁcacy beliefs, social norms and subjective resilience also affect

adaptation behaviour (medium conﬁdence), which has implications

for communication about the need for climate adaptation. Efﬁcacy

beliefs represent the belief in one’s ability to carry out particular

action(s) and the belief that the action(s) will have the desired

outcome. Belief that one is personally able to complete a behaviour

is moderately associated with engaging in disaster preparations

(Navarro et al., 2021; van Valkengoed and Steg, 2019) and with

adaptation intentions (Burnham and Ma, 2017). Collective efﬁcacy,

the belief that a group of people working together can achieve

a desired outcome, is important for participating in community

adaptation behaviours (Bandura, 1982; Chen, 2015; Thaker et al.,

2015). Related to this is response efﬁcacy, a belief that a behaviour

will achieve its desired outcome, which is also moderately associated

with engaging in disaster preparations (van Valkengoed and

Steg, 2019). Collective efﬁcacy can potentially be developed by

strengthening communication networks and social ties within a

community (Haas etal., 2021; Jugert etal., 2016). Norms describing

the adaptation strategies of others in a community, particularly

those with high social status, can either facilitate or inhibit individual

adaptation decisions (Neef etal., 2018; Smith etal., 2021).

Distinct from efﬁcacy beliefs, subjective resilience is a more general

optimism or belief about one’s ability (Jones, 2019; Khanian et al.,

2019). Subjective resilience (Clare etal., 2017) can inﬂuence preferred

responses to climate change via assessment of one’s ability to engage

in speciﬁc response options. Identities can inﬂuence assessment of

subjective resilience. Place attachment, having a strong emotional

connection to a particular location, is weakly associated with disaster

preparation (Brügger etal., 2015). In some cases, place attachment

may inhibit adaptive responses, either by reducing perceptions of risk

or by making people reluctant to leave an area that is threatened

(De Dominicis et al., 2015; van Valkengoed and Steg, 2019). Place

attachment can also contribute to enhanced community resilience

(Khanian etal., 2019; Jones, 2019; Wang etal., 2021).

7.4.4 Migration and Adaptation in the Context of

Climate Change

7.4.4.1 Linkages between Migration, Adaptation and

Household Resilience

AR5 (Chapter 17) concluded that migration is often, though not in

all situations, a potential form of adaptation initiated by households.

Subsequent research indicates that the circumstances under which

migration occurs and the degree of agency under which household

migration decisions are made are important determinants of whether

migration outcomes are successful in terms of advancing the well-

being of the household and providing beneﬁts to sending and

receiving communities (high conﬁdence) (Adger etal., 2015; Cattaneo

etal., 2019; Cross-Chapter BoxMIGRATE in Chapter 7). Evidence from

refugee studies and general migration research indicates that higher

agency migration, in which migrants have mobility options, allows

migrants greater opportunities for integrating into labour markets at

the destination, makes it easier to remit money home and generally

creates conditions for potential beneﬁts for migrant households and

for sending and receiving communities (International Organization for

Migration, 2019). Bilateral agreements that facilitate labour migration

have been identiﬁed as being especially urgently needed for Paciﬁc

small island states (Weber, 2017).

Adaptive migration and the implied assumption that people can

or should simply move out of harm’s way is not a substitute for

investment in adaptive capacity-building (high agreement) (Bettini

and Gioli, 2016). Climate-related migration, and especially involuntary

displacement, often occurs only after in situ adaptation options have

been exhausted and/or where government actions are inadequate

(Adger etal., 2015; Ocello etal., 2015; Cross-Chapter BoxMIGRATE

in Chapter 7). The threshold at which household adaptation transitions

from in situ measures to migration is highly context speciﬁc and reﬂects

the degree of exposure to speciﬁc climate risks, mobility options

and the socioeconomic circumstances of the household and local

community (McLeman, 2017; Adams and Kay, 2019; Semenza and Ebi,

2019; Cross-Chapter BoxMIGRATE in Chapter 7). A consistent theme

in the research literature reviewed for all sections of this chapter is

that proactive investments in health, social and physical infrastructure,

including those not aimed speciﬁcally at climate risks, build societal

adaptive capacity and household resilience. In turn, expanding the

range of adaptation options available to households increases the

likelihood that, when migration does occur, it does so under conditions

of high agency that lead to greater chances of success. In communities

where climate-related migration and/or relocation is occurring or may

occur, policymaking and planning beneﬁts from understanding the

cultural, social and economic needs of exposed populations and helps

in the identiﬁcation of responses and policies that build resilience

(Hino etal. 2017)

7.4.4.2 Climate, Migration and Linkages to Labour Markets and

Social Networks

Adaptive climate-related migration is often closely related to

wage-seeking labour migration (medium conﬁdence). Due to the

circumstances under which they move, climate-related migrants’

1117

Health, Wellbeing and the Changing Structure of Communities Chapter 7

destinations, labour market choices and returns from migration may be

more heavily constrained than those of other labour migrants (Jessoe

etal., 2018; Wrathall and Suckall, 2016).Within low- and middle-income

countries, rural–urban migrant networks are important channels for

remittances that may help build socioeconomic resilience to climate

hazards in sending areas (Porst and Sakdapolrak, 2020), with higher

levels of wage-seeking labour participation observed in climate-

sensitive locales in south Asia (Maharjan et al., 2020). Local-level

research in China and south Asia shows, however, that the potential

for remittances to generate improvements in household level adaptive

capacity is highly context speciﬁc, has signiﬁcant gender dimensions

and depends on such factors as the nature of the hazard, the distance

migrated and the length of time over which remittances are received

(Banerjee etal., 2019a; Banerjee etal., 2019b). Social networks are a

key asset in helping climate migrants overcome ﬁnancial and structural

impediments to their mobility, but these have their limits, particularly

with respect to international migration (Semenza and Ebi, 2019). Since

AR5, greater restrictions have emerged on movement between many

low- and high-income countries (not including those necessitated by

public health measures during the COVID-19 pandemic), a trend that,

if it continues, would generate additional constraints on destination

choices for future climate migrants (McLeman, 2019). Transnational

diasporic connections are a potential asset for building resilience

in migrant-sending communities highly exposed to climatic risks,

with migrants’ remittances potentially providing resources for long-

term resilience building, recovery from extreme events and reducing

income inequality (Bragg etal., 2018; Mosuela etal., 2015; Obokata

and Veronis, 2018; Shayegh, 2017; Semenza and Ebi, 2019). Safe

and orderly labour migration is consequently a potentially beneﬁcial

component of wider cross-sectoral approaches to building adaptive

capacity and supporting sustainable development in regions highly

exposed to climate risks (McLeman, 2019).

7.4.4.3 Attitudes Towards Climate Migration

The success of climate-related migration as an adaptive response is

shaped by how migrants are perceived and how policy discussions

are framed (high agreement, medium evidence). The possibility that

climate change may enlarge international migrant ﬂows has in some

policy discussions been interpreted as a potential threat to the security

of destination countries (Sow etal., 2016; Telford, 2018), but there is

little empirical evidence in peer-reviewed literature assessed for this

chapter of climate migrants posing signiﬁcant threats to security at

state or international levels. There is also an inconsistency between

framing in some policy discussions of undocumented migration

(climate-related and other forms) as being ‘illegal’ and the objectives

of the Global Compact on Safe, Orderly and Regular Migration and

the Global Compact on Refugees (McLeman, 2019). Although climate-

related migrants are not ofﬁcially recognised as refugees under the

1951 Convention relating to the Status of Refugees, terms such as

‘climate refugees’ are common in popular media and some policy

discussions (Høeg and Tulloch, 2018; Wiegel etal., 2019). The framing

of migration policy discussions is relevant, for example, in discussing

climate adaptation options for Paciﬁc Island Countries, where there is

considerable disagreement over policies that range from a ‘migration

with dignity’ approach that would liberalise labour migration in the

Paciﬁc region to those that see migration as a last resort option to

be avoided as much as possible (McNamara, 2015; Farbotko and

McMichael, 2019; Oakes, 2019; Remling, 2020). A more beneﬁcial

policy framing in terms of ensuring that future migration contributes to

climate resilience and sustainable development has been established

since AR5 within the framework of the Global Compact for Safe,

Orderly and Regular Migration (see Section7.4.7.7).

Attitudes of residents in migrant-receiving areas with respect to

climate-related migration warrant consideration when formulating

adaptation policy (medium conﬁdence). Existing research is modest

and difﬁcult to generalise with respect to the impacts of climate-

related migration and displacement on social dynamics and stability in

receiving destinations, with outcomes being tied to the attitudes and

social acceptance of receiving communities and efforts to integrate

migrant arrivals into the community (Koubi and Nguyen, 2020).

Research from Kenya and Vietnam shows that residents of receiving

communities view environmental drivers as being legitimate reasons

for people to move and consequently tend not to stigmatise such

migrants (Spilker et al., 2020). In these examples, urban residents

viewed environmental motivations as being comparable to economic

reasons for migrating and did not see climate-related migrants as

posing any particular risks for receiving communities. However,

case studies from India suggest that a lack of recognition by local

authorities of climatic factors being legitimate drivers of rural–urban

migration may lead to discrimination against migrants in terms of

access to housing and other social protections, thereby undermining

household resilience (Chu and Michael, 2018).

7.4.4.4 Planned Relocation and Managed Retreats

There is high agreement among existing studies that immobile

populations often have high vulnerability and/or high long-term exposure

to climate hazards, and that non-climatic political, economic and social

factors within countries may strongly constrain mobility (Zickgraf,

2019; Ayeb-Karlsson etal., 2020; Cundill etal., 2021). Section7.2.6.2

highlighted the particular vulnerability of immobile populations in the

face of growing climatic risks. However, research suggests governments

should be slow to label such populations as being ‘trapped’ or to actively

promote relocations in the absence of local agreement that in situ

adaptation options have been exhausted (Adams, 2016; Farbotko and

McMichael, 2019). In the case of indigenous settlements, efforts made

to incorporate traditional knowledge in decision-making and planning

increase the potential for longer-term success (Manrique, 2018).

Considerable health implications can emerge within populations that

are relocated as part of a planned retreat, and represent an important

consideration for planners that requires greater research (Dannenberg

etal., 2019). Organised relocations are not inherently transformative

in their outcomes but, depending on the circumstances under which

they occur and on how issues of equity and respect for the rights of

those affected are implemented, relocation could potentially represent

a positive transformation (Siders etal., 2021).

Disruptive and expensive relocations of low-lying coastal settlements in

many regions would become increasingly necessary in coming decades

under high levels of warming (high conﬁdence). Organised relocations

require long-term innovation, planning and cooperation on the part of

governments, institutions, affected populations and civil society (Hauer,

1118

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

2017; Hino et al., 2017; Haasnoot et al., 2021; Moss et al., 2021).

Recent examples illustrate the substantial ﬁnancial costs of organised

relocations, ranging from USD 10,000 per person in examples from Fiji

to USD 100,000 per person in coastal Louisiana, USA (Hino etal., 2017).

Organised relocations are politically and emotionally charged, may

not necessarily be seen as desirable by exposed populations and are

most successful when approached proactive and strategically to avoid

increasing the socioeconomic vulnerability of those who are relocated

(Jamero etal., 2017; Wilmsen and Webber, 2015; Chapin etal., 2016;

McNamara et al., 2018; Hauer et al., 2019; Bertana, 2020). Key

considerations for protecting the rights and well-being of people who

might need to be resettled include proactive communication with and

participation of the affected communities, availability of compensation,

livelihood protection and ensuring there is permanence and security of

tenure at the relocation destination (Tadgell etal., 2018). Availability

of funds for resettlement, how to manage relocation from communally

owned lands, how to value privately owned land to be abandoned and

the potential for loss and damage claims are just some of the many

potential complications (Marino, 2018; McNamara et al., 2018). As

a proactive option, researchers in Bangladesh have suggested the

creation of ‘migrant-friendly towns’ to provide options for autonomous

relocation from hazardous areas (Khan and Huq, 2021).

7.4.5 Adaptation Solutions for Reducing Conﬂict Risks

There has been increased activity within the international community

to understand and address climate–conﬂict linkages since AR5, with

high level actions including the UN Climate Security Mechanism,

launched in 2018 and tasked with providing integrated climate

risk assessments to the United Nations Security Council and other

UN bodies in partnership with UN and external actors (DPPA etal.,

2020). G7 governments initiated an integrated agenda for resilience

(Rüttinger etal., 2015) and the Berlin Call for Action in 2019 sought a

foreign policy platform to address climate security concerns, focusing

on risk-informed planning, enhanced capacity for action within the UN

and improvements to operational response to climate security risks

(Federal Foreign Ofﬁce, 2019). The non-peer-reviewed literature that

currently addresses these policy dimensions is often generated by a

small number of consultancies funded by governments from the Global

North and can lack diverse perspectives and priorities.

7.4.5.1 Environmental Cooperation and Peacebuilding

The environment can form the basis for active peacebuilding, and

a sustainable natural environment is important for ongoing peace

(high agreement, medium evidence). EP is a framework increasingly

utilised to understand the diverse ways in which the natural

environment supports peace and can be utilised in peacebuilding;

key tenets include preserving the natural environment such that

degradation does not contribute to violence, protecting natural

resources during conﬂict and using natural resources to support

post-conﬂict economic recovery (Kron, 2019). EP frames natural

resources as facilitating peace rather than driving conﬂict (Dresse

et al., 2019) with emerging literature analysing what this means

in practice (Kovach and Conca, 2016; Krampe, 2017; Ide, 2019;

Ide etal., 2021; Johnson, 2021; Kalilou, 2021). There is emergent

evidence for the success of EP pathways. For example, a natural

resource sharing agreement on the Kenya–Uganda border was able

to reconcile spatial, logistical and conceptual barriers to addressing

climate risks in development contexts (Abrahams, 2020). However,

the long-term impacts of EP approaches on sustaining peace are

yet to be monitored and evaluated (Ide and Tubi, 2020). EP may be

successful depending on the context and the element of peace being

built (Johnson, 2021) or undermine processes when environmental

arguments are co-opted for geopolitical purposes (Barquet, 2015) or

to depoliticise conﬂict (Ide, 2020).

Formal institutional arrangements for natural resource management

can contribute to transnational cooperation (high conﬁdence) (see also

Chapter 4). Evidence from transboundary water sharing agreements

provides evidence for cooperation rather than conﬂict over resources

(Timmerman et al., 2017; Timmerman, 2020; Dinar et al., 2015).

Transboundary water agreements and river basin organisations help

build robust institutions that facilitate trust and relationship building

that have beneﬁts in other domains (strong agreement, medium

evidence) (Dombrowsky, 2010; Krampe and Gignoux, 2018; Barquet

etal., 2014; Ide and Detges 2018). However, outcomes can be mixed,

and the international and top down nature of these approaches may

limit their transferability to intra-state conﬂicts at local levels (Rigi and

Warner, 2020; Ide etal., 2021; Krampe etal., 2021).

7.4.5.2 Adaptation in Fragile Settings

Climate-resilient peacebuilding has the potential to limit the impact

of future climate change on peace efforts (medium conﬁdence).

Practical guidance has been developed, driven by policy concerns on

climate–conﬂict links. The United Nations Environment Programme

(UNEP), the European Union and Adelphi have developed a toolkit

for addressing climate fragility risks in peacebuilding, adaptation

and livelihoods support (UNEP etal., 2019). Crawford etal. (2015)

provide recommendations for climate-resilient peacebuilding

consistent with the UN Secretary General’s ﬁve peacebuilding

principles, including integrating ex-combatants through the

construction of climate-resilient infrastructure, using climate impacts

as a platform to engage previously conﬂicting groups, developing

national DRR and management strategies, and climate-prooﬁng

economic development activities. The USAID, in a report prepared

for the Adaptation Thought Leadership and Assessments (ATLAS)

programme (Adelphi & Chemonics International, 2020) that drew

upon resilience and peacebuilding programmes in the Horn of Africa,

recommend two critical conditions to ensure activities address

compound climate fragility risks. Firstly, conducting local analyses

of the links between climate, conﬂict and fragility to identify speciﬁc

risks to target and, secondly, ensuring long-term commitment with a

focus on participation and ﬂexibility.

Conﬂict-sensitive adaptation that focuses on institutional frameworks,

conﬂict management and governance mechanisms has the potential

to address complex interacting risks and emergencies over the long

term (medium agreement, limited evidence) (Scheffran et al., 2012;

Matthew, 2018; Okpara et al., 2018). However, most adaptation

activities are planned and implemented under development or climate

ﬁnance funds without systematic integration of conﬂict sensitivity,

1119

Health, Wellbeing and the Changing Structure of Communities Chapter 7

and National Adaptation strategies rarely and only implicitly address

conﬂict and potential changes to power relations (Tänzler etal., 2019).

Practitioners and policy researchers have attempted to address this

gap by developing guidance and delivering training (e.g., Tänzler etal.

(2019); Bob and Bronkhorst (2014)). However, there are real challenges

relating to discounting indirect impacts on conﬂict and maladaptation

(Asplund and Hjerpe, 2020) and risks of unintended outcomes

(Mirumachi et al., 2020). Crawford and Church (2020) highlight the

synergies between adaptation planning under the UNFCCC’s National

Adaptation Plan process and conﬂict reduction. Discussing development

more broadly, Abrahams (2020) suggests three barriers to development

that incorporate conﬂict–climate risks: geographically disconnected

impacts and outcomes, the discourse of climate as a threat multiplier

(rather than underlying peace) and teleconnected risks occurring at

different scales. Effective approaches rely on understanding local power

dynamics and social relations (Sovacool 2018; Roth etal. 2019; Sapiains

etal. 2021) (high agreement, medium evidence).

7.4.5.3 Gender-Based Approaches to Peacebuilding

Gender-based approaches provide novel under-utilised pathways

to achieving sustainable peace (high conﬁdence). Security council

resolutions have encouraged the incorporation of gender analysis into

peacebuilding and research has shown that taking into account the

gendered nature of networks and dialogues opens new avenues for

cooperation that are conﬂict sensitive (Dunn and Matthew, 2015),

creating potential for women’s rights and advocacy groups to be drivers

of peace (Céspedes-Báez, 2018). For example, women are working to

reduce climate vulnerability security risks in urban settings by entering

local politics and joining community-based organised and civil society

networks (Kellog, 2020). The gendered nature of vulnerability and

access to natural resources (Sections4.6.4, 4.7.5.3, 5.4.2.3, 5.5.2.6,

5.8.2.2; Cross-Chapter BoxGENDER in Chapter 18) will inﬂuence the

efﬁcacy of interventions to prevent conﬂict or to build durable peace

(Pearse, 2017; Chandra etal., 2017; Fröhlich etal., 2018). However, this

understanding has not so far resulted in widespread employment of

gender-led analyses (Fröhlich and Gioli, 2015). This represents a key

opportunity for expansion of the solution space for climate-related

conﬂict. Analysis of peace processes more generally demonstrates

the beneﬁts of women’s participation in peace processes for devising

strategies for building peace (Paffenholz, 2018; Cárdenas and Olivius,

2021) and for the durability of that peace (Shair-Rosenﬁeld and Wood,

2017; Krause etal., 2018).

7.4.6 Climate Resilient Development Pathways

Climate resilient development is a set of trajectories that strengthens

sustainable development and efforts to eradicate poverty and reduce

inequalities while promoting fair and equitable reductions of GHG

emissions. Climate resilient development also serves to steer societies

towards low-carbon, prosperous and ecologically safer futures

(Chapter 1). All pathways to pursue climate resilient development

will involve balancing complex synergies and trade-offs (very high

conﬁdence; Chapter 18). Pathways to climate resilient development

can be pursued simultaneously with recovering from the COVID-19

pandemic (Cross-Chapter BoxCOVID in Chapter 7; Ebi etal., 2021).

Meeting commitments against the following seven existing global

priorities would facilitate CRDPs and transformational futures for

health, well-being, conﬂict and migration (high agreement, medium

evidence):

i) Fully implementing the WHO Operational Framework for building

climate-resilient health systems (WHO, 2015b)

ii) Achieving Universal Health Coverage (UHC) under SDG 3 (good

health and well-being)

iii) Achieving net zero GHG emissions from healthcare systems and

services

iv) Achieving the SDGs more generally

v) Adopting mitigation policies and technologies that have signiﬁcant

health co-beneﬁts (see Cross-Chapter BoxHEALTH)

vi) Meeting the objectives of the Global Compact for Safe, Orderly and

Regular Migration

vii) Inclusive and integrative approaches to climate-resilient peace

These transformations map across all ﬁve of the system transitions

identiﬁed in Chapter 18: energy systems; land, ocean, and ecosystems;

urban and infrastructural systems; industrial systems; and societal

systems.

7.4.7.1 Fully Implementing the World Health Organization

Operational Framework

The WHO Operational Framework for building climate-resilient health

systems was designed to increase the capacity of health systems

and public health programming to protect health in an unstable and

changing climate (WHO, 2015b). The guidance deﬁnes a climate-

resilient health system as one that is capable to anticipate, respond

to, cope with, recover from and adapt to climate-related shocks and

stress, so as to bring sustained improvements in population health

despite an unstable climate. Full implementation of this framework has

the potential to achieve transformational adaptation; the fundamental

attributes of health systems would change to anticipate and effectively

manage the population health and healthcare risks of climate change.

This includes having the knowledge, capacity, tools and human and

ﬁnancial resources for health systems to extend beyond soft limits to

adaptation.

The WHO framework outlines 10 key components (Figure7.15) that,

when achieved, will:

• Guide professionals working in health systems and in health

determining sectors (e.g., water and sanitation, food and

agriculture, energy, and urban planning) to understand and

effectively prepare for the additional health risks posed by climate

variability and change

• Identify the main health functions that need to be strengthened to

build climate resilience, and to use these to develop comprehensive

and practical plans (e.g., the health component of National

Adaptation Plans (H-NAP))

• Support health decision makers to identify roles and responsibilities

to implement this plan for actors within and outside the formal

health sector

1120

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Achieving full implementation of the WHO Operational Framework

requires determination and commitment—with associated funding—

from the health community speciﬁcally and health-determining sectors

more generally. Identifying priority areas is an immediate step required

to commence this implementation process, which will vary across

different contexts. Active engagement with Communities of Practice to

share lessons and experiences would be a useful approach to support

national and sub-national efforts; examples of this already exist (e.g., the

Climate Change Community of Practice in Canada and the ‘weADAPT’

initiative under the auspices of the Stockholm Environment Institute).

Table7.9 summarises selected characteristics of health systems as they

might be under SSP1 (a world aiming to sustainable development),

SSP2 (a world continuing current trends) and SSP3 (a world with high

challenges to adaptation and mitigation), with systems under SSP1 being

most consistent with climate resilient development. The table highlights

the importance of investments that promote sustainable and resilient

development to decrease vulnerability, no matter the magnitude and

pattern of climate change. Adapting under SSP3 would be challenging

even under pathways of limited additional climate change.

Stress testing is an approach for evaluating the extent to which health

systems are prepared for a future different from today (Ebi etal., 2018a).

These desk-based exercises identify a desirable future outcome, such

as successfully managing an extreme heatwave, ﬂood or storm with

characteristics outside the range of recent experiences. The exercises

move beyond identifying likely challenges from hazardous exposures

to specifying policies and measures that could be successful under a

different climate and development pathway. The exercises consider

socioeconomic and political factors that can inﬂuence the extent of

health system vulnerability and other factors that can affect health

system demands by impacting population health. Stress testing is

designed to identify conditions under which it would be difﬁcult for

the health system to maintain its essential functions and to identify

interventions that could maintain essential system functions despite

climate-related shocks and stresses.

Ten components of the WHO operational framework for building climate resilient health systems,

with links to the building blocks of health systems.

Financing

Leadership &

governance

Health

workforce

Health

information

systems

Essential

medical

products &

technolologies

Service

delivery

rkf

Financin

CLIMATE &

HEALTH FINANCING

LEADERSHIP &

GOVERNANCE

HEALTH

WORKFORCE

VULNERADBILITY,

CAPACITY &

ADAPTATION

ASSESSMENT

Building blocks

of health systems

INTEGRATED RISK

MONITORING & EARLY

WARNING

HEALTH & CLIMATE

RESEARCH

CLIMATE RESILIENT &

SUSTAINABLE

TECHNOLOGIES AND

INFRASTRUCTURE

MANAGEMENT OF

ENVIRONMENTAL

DETERMINANTS OF

HEALTH

CLIMATE-INFORMED

HEALTH

PROGRAMMES

EMERGENCY

PREPAREDNESS &

MANAGEMENT

Figure7.15 | Ten components of the WHO operational framework for building climate-resilient health systems with links to the building blocks of health

systems. Source: WHO (2015b).

1121

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Table7.9 | Characteristics of future health systems under three SSPs; modiﬁed from Sellers and Ebi (2017).

SSP3 SSP2 SSP1

Basic characteristics

Reactive; failure to adapt; siloed information

channels and national governance; limited

partnerships

Incomplete planning; new information incorporated

as convenient; occasional partnerships

Proactive; adaptively managed; frequent

partnerships; inter-disciplinary

Leadership and

governance

Little focus at national and international levels

on climate change and health; minimal planning

conducted

Planning for climate change and health, but not

comprehensive and often side-tracked by other

issues

Strong climate change and health planning

apparatus, including health components of

national adaptation plans; regional/international

partnerships

Health workforce

Climate change and health not often incorporated

into training; few provisions for new training

programmes or funding for increase health worker

positions in climate change-relevant specialties;

health disparities not addressed

Climate change and health not systematically

incorporated into training; new training

programmes insufﬁcient to ﬁll gaps in demand;

limited attention to addressing health disparities

Systematic inclusion of climate change and health

in worker training; expansion of funding and

training; ﬁnancing and incentive mechanisms to

address health disparities

Health information

systems

Assessments of vulnerability and adaptation rarely

conducted, if ever; information not useful for

planning; minimal risk monitoring or research

Vulnerability and adaptation assessments

occasionally conducted, but generally of poor

quality; early warnings incomplete; ﬁscal and

political constraints on research

Vulnerability and adaptation assessments regularly

conducted and used in planning; robust early

warning networks; research agenda focused on

vulnerable communities

Climate-resilient

and sustainable

technologies and

infrastructure

Facilities sited and constructed without climate

consideration incorporated; medical supply chains

not modiﬁed

Capital cost serves as a key factor in siting and

construction; increasing vulnerability of facilities

to shocks

Health infrastructure designed to be robust to

storms/ﬂoods, with redundant systems added to

ensure continuity of care

Service delivery

Policies to manage environmental health hazards

generally not followed; care practices not modiﬁed

to accommodate climate information; few changes

to emergency management procedures; health

inequities worsen

Environmental health policies are not robust;

marginal improvements in care practices; risk

assessments and communication inadequate; no

shift in health inequities

Policies to manage environmental health hazards

regularly reviewed; practitioners review care

practices and adjust as appropriate based on

local climate and health conditions; robust

communication tools developed; health service

improvements reduce health inequities

Climate and health

ﬁnancing

Few funds devoted to climate change and health

activities, particularly in low- and middle-income

countries; few if any ﬁnancing partnerships between

high-, low- and middle-income countries; very weak

regional and international coordinating bodies due

to funding constraints

High-income countries generally form robust

ﬁnancing mechanisms; ﬁscal pressures in low- and

middle-income countries constrain their ﬁnancing

abilities; ﬁnancial partnerships formed across

countries, but ﬁnancing often not robust; regional

and international coordinating bodies receive

inadequate funds

Robust funding streams for climate change and

health; climate change and health activities

receive continuing ﬁnancial support; effective

ﬁnancing partnerships; regional and international

coordinating bodies effectively funded

7.4.6.2 Achieving Universal Health Coverage Under SDG 3

(good health and well-being)

UHC is when all people have access to the health services they need,

when and where they need them, without ﬁnancial hardship (WHO,

2021b). Achieving UHC is one of the targets in the SDGs. However,

climate change is threatening to undermine the achievement of UHC

through negative health outcomes and healthcare system disruptions

(Salas and Jha, 2019; Phillips et al., 2020; Kadandale et al., 2020;

Roa et al., 2020). Climate change adaptation and UHC progress

are closely linked to one another, as both may improve health and

achieve health equity (Salas and Jha, 2019). Supporting UHC is key

to securing population health under a changing climate as well as

addressing structural inequalities (Roos et al., 2021; Aleksandrova,

2020; Phillips etal., 2020). Many regions of the world with the highest

levels of vulnerability to the health impacts of climate change also

have low levels of UHC; an integrated approach to UHC planning that

incorporates climate change will have great beneﬁts particularly in

improving health equity (Salas and Jha, 2019).

The COVID-19 pandemic has shown some countries taking positive steps

to achieving UHC. For example, Ireland nationalised healthcare for the

duration of the pandemic and many countries, including Australia, have

enhanced their telehealth services, which has enabled speciﬁc groups

to access health services, particularly those in rural and remote settings,

and has allowed continuous care to the community (Monaghesh and

Hajizadeh, 2020; Cross-Chapter BoxCOVID in Chapter 7).

7.4.6.3 Achieving Net Zero GHG Emissions from Healthcare

Systems and Services

The healthcare system is a core component of UHC, supporting

climate-resilient and environmentally sustainable healthcare facilities

(Corvalan et al., 2020). Health systems are large carbon polluters

and have the potential to look beyond traditional ‘green’ initiatives

towards a more fundamental, longer-term redesign of current

service models, with health practitioners participating actively in this

process (Charlesworth and Jamieson, 2018). In the largest and most

comprehensive accounting of national healthcare service emissions,

the UK’s National Health Service (NHS) quantiﬁed its health services’

emissions and identiﬁed that 62% came from the supply chain, 24%

from the direct delivery of care, 10% from staff commute and patient

and visitor travel, and 4% from private health and care services

commissioned by the NHS (Tennison etal., 2021).

1122

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

The health sector has considerable opportunity to reduce its own

carbon footprint and by doing so would contribute to mitigation efforts

and help reduce health burdens associated with GHG emissions (Vidal

etal., 2014; Duane et al., 2019; Charlesworth and Jamieson, 2019;

Charlesworth etal., 2018; Guetter etal., 2018; Bharara etal., 2018;

Frumkin, 2018) (high conﬁdence). The UK’s NHS has committed to

becoming the world’s ﬁrst net zero national healthcare system. Other

examples of recent and ongoing initiatives include those undertaken

by the Kaiser Permanente and the Gundersen Clinics in the USA, Health

Care without Harm in the Asia Paciﬁc region, and the Green Hospital

Initiative in New Delhi (Frumkin, 2018; Bharara etal., 2018).

7.4.6.4 Achieving the SDGs Would Increase Resilience in

Health-Determining Sectors and Contribute to Reducing

the Risks of Involuntary Displacement and Conﬂict

The SDGs are globally agreed objectives that integrate the economic,

environmental and social aspects of sustainable development to end

poverty, protect nature and ensure that all people enjoy peace and

prosperity. The SDGs were developed under the principle that the

goals are integrated and indivisible, such that progress in one goal

depends on progress in others (WHO, 2016b). Promoting health and

well-being is not the sole responsibility of the health sector; it is also

partially determined by strategies, policies and options such as poverty

reduction, promoting gender equality, ensuring all people enjoy

peace and prosperity, eliminating nutritional insecurity and ensuring

availability and sustainable management of water and sanitation

(Morton et al., 2019; Bennett et al., 2020). Unique themes in the

SDGs for health policy and systems research include social protection,

access to health services, stronger and more effective multi-sectoral

collaborations beyond the health sector to address the upstream

drivers of health and well-being, and participatory and accountable

institutions to strengthen civic engagement and local accountability

within health systems (Bennett etal., 2020).

For example, clean water, sanitation and hygiene are essential to

human health and well-being. Unsafe water and sanitation and a lack

of hygiene caused an estimated 870,000 associated deaths in 2016

(WHO, 2021c). Only 71% of the global population has access to safely

managed drinking water services; only 45% of the global population

has access to safely managed sanitation services; and 60% has basic

handwashing facilities in their home. About 25% of healthcare facilities

lack basic water services, exposing workers and patients to higher

infection risks. More than 80% of countries reported in 2018 that they

lacked sufﬁcient funding to meet national WASH targets. As detailed in

Section7.2.2.2, Box7.3, Section7.3.1.4 and Section7.4.2.3, the burden

of climate-sensitive WBDs would be reduced if WASH targets were met.

WHO developed a Global Action Plan for Healthy Lives and Well-Being

for All that brings together multi-lateral health, development and

humanitarian agencies to support countries in accelerating progress

towards the health-related SDGs (WHO, 2021c). Themes include

sustainable ﬁnancing to reduce unmet needs for services, community

and civil society engagement to generate knowledge to inform

policymaking and health responses, addressing the socioenvironmental

determinants of health, ensuring health and humanitarian services are

available in fragile and vulnerable settings, research and development,

and greater implementation of digital health delivery. In 2020,

enhanced collaboration through the Global Action Plan provided

support for an equitable recovery from the COVID-19 pandemic in,

for example, Lao People’s Democratic Republic, Pakistan, Tajikistan,

Somalia, South Sudan, Malawi, Nepal and Columbia, highlighting the

potential for multi-sectoral integration of economic, environmental

and social aspects of sustainable development to maintain essential

health services and core public health functions during shocks and

stresses (WHO, 2021a).

Meeting the SDGs also contributes towards reducing involuntary

displacement and conﬂict, as assessed in Sections7.4.6.6 and 7.4.6.7.

7.4.6.5 Adopting Mitigation Policies and Technologies that

Have Signiﬁcant Health Co-beneﬁts

Substantial co-beneﬁts from climate action can result from investing

in health systems, infrastructure, water and sanitation, clean energy,

affordable healthy diets, low-carbon housing, public transport,

improved air quality, and social protection. These beneﬁts are in

addition to the avoided health impacts associated with climate change

(see Cross-Chapter BoxHEALTH in Chapter 7).

7.4.6.6 International Policy Frameworks for Migration that

Contribute to Climate Resilient Development

Climate-related migration, displacement and immobility in coming

decades will coincide with global and regional demographic changes

that will produce a widening distinction between high-income

countries that have aging, slow-growing (or in some countries,

shrinking) population numbers and low-income countries that have

rapidly growing, youthful populations. Given this dynamic, coordinated

national and international strategies that integrate migration and

displacement considerations with wider adaptation and sustainable

development policies may contribute to climate resilient development.

Since AR5, the international community has established a number

of agreements and initiatives that, with continued pursuit and

implementation, would create potential for climate-related migration

to be a positive contribution towards adaptive capacity-building and

sustainable development more broadly (Warner, 2018).

The 2018 Global Compact for Safe, Orderly and Regular Migration

provides an important opportunity for planning for and responding

to future climate-related migration and displacement (Kälin, 2018).

Among its 23 objectives, the Compact explicitly encourages the

international community to implement migration policies that facilitate

voluntary migration and actively prepare for involuntary displacements

due to climate change, especially in low- and middle-income countries.

The Compact’s objectives include reducing barriers to legal and safe

migration, and facilitating the freer ﬂow of remittances between sending

and receiving communities. By doing so the Compact aims to increase

the potential for migration to make positive contributions to sustainable

development and to adaptive capacity-building. It also contains speciﬁc

provisions pertaining to climate- and disaster-related migration and

displacement. Objective 2 of the Compact aims at reducing drivers

of involuntary or low-agency migration and recommends that states

establish systems for sharing information on environmental migration,

1123

Health, Wellbeing and the Changing Structure of Communities Chapter 7

develop climate adaptation and resilience strategies harmonised

at sub-regional and regional levels, and cooperate on disaster risk

prevention and response. Other objectives in the Compact relevant

to climate-related migration include Objective 5 (increasing pathways

for regular migration) and Objective 19 (facilitating migrants’ ability

to contribute to sustainable development). Objective 18, which links

migration with skills development, is consistent with the ‘migration with

dignity’ approach to displacement risks (McNamara, 2015; Kupferberg,

2021). The 2018 Global Compact on Refugees observes that climate

hazards increasingly interact with the drivers of refugee movements.

The guidelines this Compact provides to governments regarding actions

for addressing the causes of refugee movements and considerations for

assisting and supporting refugees are useful for governments seeking

guidance for all forms of displacement more generally, including

displacement linked to climate change.

Pursuant to the Paris Agreement, a task force was struck by the

Warsaw International Mechanism to make recommendations to the

Conference of the Parties to the UNFCCC on how to reduce the risks

of climate-related displacement. Its 2018 report recommended that

parties work towards development of national legislation, cooperate

on research, strengthen preparedness, integrate mobility into wider

adaptation plans, work towards safe and orderly migration, and

provide assistance to people internally displaced for climate-related

reasons. Such recommendations dovetail strongly with the objectives

of the Compacts on Migration and Refugees as well as the Sendai

Framework for DRR and the 2030 SDGs. The SDGs, which include

multiple goals and targets in which migration plays an explicit role

in fostering development (Nurse, 2019), may be seen as completing

the international policy arrangements necessary for addressing future

climate-related migration and displacement.

7.4.6.7 Inclusive and Integrative Approaches to Climate-

Resilient Peace

CRDPs to reduce conﬂict risk rely on a shift in perspective from framings

around resource scarcity and security to sustainable natural resource

governance and peace (Brauch et al., 2016; Barnett, 2018; Dresse

et al., 2018; Day and Caus, 2020). Recognising that conﬂict results

from underlying vulnerabilities, development that reduces vulnerability

offers the best win-win option for building sustainable, climate-

resilient peace rather than speciﬁc security-focused interventions (high

conﬁdence). To this end, meeting the SDGs represents an unambiguous

path to reducing conﬂict risk in a climate-changed world (Singh and

Chudasama, 2021). There is growing acceptance in the development

community, despite reservations about the securitisation of climate,

that instability and conﬂict exacerbated by climate change has the

potential to undermine development gains (Casado-Asensio et al.,

2020; Day and Caus, 2020).

Core to achieving climate-resilient peace are new ways of working that

involve cross-issue and cross-sectoral collaboration and integration as

a default to policy and programming. The Security Council Resolution

1325 Women and peace and security (S/RES/1325 (2000)) and the

Sustaining Peace Agenda (A/RES/70/262 (2016)) are notable examples

of this. The 2020 UNEP report on gender and security recommends

integrated policy frameworks, better ﬁnancing to strengthen women’s

roles in peacebuilding, integrated programme design, and further

research on gender, climate and security linkages. Inclusive approaches

recognise that much of the vulnerability that drives conﬂict risk

is generated by existing inequality and marginalisation of large

proportions of the population—for example women and youth—and

that peace cannot be achieved without their needs being taken into

account and without their participation in peace processes (Mosello

etal., 2021). Diverse and inclusive partnerships also require ways to

better engage local-level participation, and improve understanding of

how to build consensus through human rights-based approaches that

recognize non-violent conﬂict and protest to be potentially positive

and constructive elements of transformational approaches to building

resilience (Nursey-Bray, 2017; Ensor etal., 2018; Schipper etal., 2021).

Addressing the lack of participation of researchers and experts from

countries most at risk of conﬂict in many climate-related conﬂict and

peacebuilding assessments and initiatives could also support this

objective. There is an increasing focus on the role of environmental

defenders in highlighting violations and gaps in state obligations

through non-violent protest (Butt etal., 2019; Scheidel etal., 2020).

CRDPs for sustainable peace also require different ways of gathering

intelligence and informing conﬂict risk. Dynamics that affect such

risks exist across scales from the local to the regional, and require

response in a transboundary manner. There is increasing emphasis on

engaging local stakeholders and diverse partnerships to inform context

appropriate measures and better policy coordination (Bremberg etal.,

2019; Tshimanga et al., 2021; Abrahams, 2020). The UN’s Climate

Security Mechanism, working across three UN departments, takes an

integrated approach to analyse and support timely and appropriate

responses to conﬂict risk, focusing on risk assessments and early

warning systems to aid conﬂict prevention, climate-informed peace and

security activities and conﬂict-sensitive development, and to promote

inter-sectoral cooperation, partnership and information sharing (DPPA

etal., 2020). There is already acknowledgement that adaptation needs

to be effectively monitored so that maladaptation can be avoided

(Eriksen etal., 2021). Here, academic research, which until now has

predominantly focused on understanding the causal relationship

between conﬂict and climate, could contribute to advancing the

monitoring and evaluation of climate-resilient peacebuilding initiatives

(Mach etal., 2020; Gilmore etal., 2018).

1124

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Cross-Chapter BoxHEALTH | Co-beneﬁts of Climate Actions for Human Health, Well-Being and

Equity

Authors: Cristina Tirado (USA/Spain, Chapter 7); Robbert Biesbroek (Netherlands, Chapter 13); Mark Pelling (United Kingdom, Chapter 6);

Jeremy Hess (USA, Chapter 7); Felix Creutzig (Germany, WGIII); Rachel Bezner Kerr (Canada/USA, Chapter 5); Siri Eriksen (Norway, Chapter

18); Diarmid Campbell-Lendrum (United Kingdom, Chapter 7); Elisabeth Gilmore (USA/Canada, Chapter 14); Maria Figueroa (Denmark/

Venezuela, WGIII); Nathalie Hilmi (Monaco, Chapter 18); Peter Newman (Australia, WGIII); Sebastian Mirasgedis (Greece, WGIII); Sharma

Rohit (India); Yamina Saheb (France/Algeria, WGIII); Gerardo Sanchez Martinez (Spain); Peter Smith (United Kingdom, WGIII); Adrian Leip

(Italy, WGIII); Dhar Subash (Denmark/India, WGIII); Chris Trisos (South Africa, Chapter 9); Mercedes Bustamante (Brazil, WGIII); Luisa

Cabeza (Spain, WGIII); Diana Urge-Vorsatz (Hungary, WGIII)

Achieving the Paris Agreement and SDGs can result in low-carbon, healthy, resilient and equitable societies with high well-being for all (very

high conﬁdence) (Alfredsson etal., 2018; O’Neill etal., 2018). Given the overlap in sources of greenhouse gases (GHGs) and co-pollutants

in energy systems, strategies that pursue GHG emission reductions and improvements in energy efﬁciency hold signiﬁcant potential health

co-beneﬁts through air pollution emission reductions (high conﬁdence) (Gao etal., 2018). Air quality improvements alone can substantially

offset, or most likely exceed, mitigation costs at the societal level (Schucht etal., 2015; Chang etal., 2017; Markandya etal., 2018; Vandyck

etal., 2018; Peng etal., 2017; Woodward etal., 2019; Sampedro etal., 2020; Xie etal., 2018). Pursuit of a mitigation pathway compatible

with warming of +1.5°C with associated cleaner air, avoided extreme events and improved food security and nutrition could result in 152

± 43million fewer premature deaths worldwide between 2020 and 2100 compared with a business-as-usual scenario (Shindell etal.,

2018). Reaching the Paris Agreement could result in an annual reduction of 1.18million air pollution-related deaths, 5.86million diet-

related deaths and 1.15million deaths due to physical inactivity across nine major economies by 2040 (Hamilton etal., 2021). In Europe,

a mitigation scenario compatible with RCP2.6 could reduce total pollution costs, mostly from PM2.5, by 84%, with human health beneﬁts

equal to more than Euro 1 trillion over ﬁve years (Scasny etal., 2015). In the EU, ambitious climate mitigation policies could reduce years

of lost life due to ﬁne particulate matter (PM) from over 4.6million in 2005 to 1million in 2050, reduce ozone-related premature deaths

from 48,000 to 7,000 and generate health beneﬁts of Euro 62billion yr

–1

in 2050 (Schucht etal., 2015).

However, there may be signiﬁcant trade-offs between mitigation and other societal goals (Dong etal., 2019; Gao et al., 2018). In

some scenarios, mitigation policies consistent with the NDCs may slow poverty reduction efforts (Campagnolo and Davide, 2019) with

implications for health. A framework of ‘co-impacts’ that assumes neither a general beneﬁcial nature of all implications from mitigation

policy nor a hierarchy between climate and other types of beneﬁts, may be more appropriate (Ürge-Vorsatz etal., 2014; Cohen etal., 2017).

Transitioning to affordable clean energy sources for all presents opportunities for substantial well-being, health, and equity co-beneﬁts

(high conﬁdence) (Gibon etal., 2017; Lacey etal., 2017; Peng etal., 2018; Vandyck etal., 2018; Williams etal., 2018). Residential solid fuel

use affects health and degrades indoor air quality for up to 3.1billion people in low- and middle-income countries (WHO, 2016b; Wang

etal., 2017a). Adherence to planned emission reductions from the Paris Agreement related to renewables could subsequently improve air

quality and prevent 71,000–99,000 premature deaths annually by 2030 (Vandyck etal., 2018). This effect increases with a 2°C pathway,

with 0.7–1.5million premature deaths avoided annually by 2050 (Vandyck etal., 2018). Co-beneﬁts are also observed at national and

regional levels. For instance, China could expect 55,000–69,000 averted deaths in 2030 if it transitioned to a half-decarbonised power

supply for its residential and vehicle sectors (Peng etal., 2018).

Investing in universal basic infrastructure, including sanitation, clean drinking water, drainage, electricity, and land-rights, can transform

development opportunities, increase adaptive capacity, and reduce vulnerability to climate-related risks (high agreement, high evidence).

Transformative approaches that reduce climate-related risks and deliver enhanced social inclusion and development opportunities for

the urban poor are most likely where local governments act in partnership with local communities and other civil society actors (high

conﬁdence) (Chapter 6, sections 6.1, 6.3, 6.4).

Rapid urbanisation offers a time-limited opportunity to work at scale towards transformational adaptation and climate resilient

development (medium evidence, high agreement). Multi-level leadership, institutional capacity and ﬁnancial resources to support

inclusive adaptation in the context of multiple pressures and inter-connected risks can help ensure that the additional 2.5billion people

projected to live in urban areas by 2050 are less exposed to climate-related hazards and contribute less to global warming (high

conﬁdence) (Chapter 6, sections 6.1, 6.3, 6.4). Integrating low-carbon, inclusive adaptation into infrastructure investment driven by rapid

urban population growth and COVID-19 recovery can accelerate co-beneﬁts (Chapter 6).

Urban planning that combines clean, affordable public transportation, shared clean vehicles and accessible active transportation modes

can improve air quality and contribute to healthy, equitable societies and higher well-being for all. Stimulating active mobility (walking

and bicycling) can bring physical and mental health beneﬁts (high conﬁdence) (Chapter 6; Rojas-Rueda etal., 2016; Avila-Palencia etal.,

1125

Health, Wellbeing and the Changing Structure of Communities Chapter 7

2018; Gascon etal., 2019; Hamilton etal., 2021). The health gains from active mobility outweigh trafﬁc-related injuries due to a decreased

incidence of chronic diseases (Ahmad etal., 2017; Maizlish etal., 2017; Tainio etal., 2017; Woodcock etal., 2018).

Urban green and blue spaces contribute to climate change adaptation and mitigation and improve physical and mental health and

well-being (high conﬁdence) (Hansen 2017; EC, 2018; WHO, 2018a; Rojas-Rueda etal. 2019; 13.7.3, WGII; 6. WGII; 8.4 WGIII). Urban

green infrastructure including urban gardens, can bring beneﬁts to social cohesion, mental health and well-being and reduce the health

impacts of heatwaves by decreasing temperatures, thus reducing inequities in exposure to heat stress for low income, marginalised

groups (Hoffman etal., 2020; Hoffmann etal., 2020; Chapter 5 section 5.12.5; Chapter 6; Chapter 7 section 7.4; Chapter 13 section 13.7).

The trade-offs of increasing urban green and blue spaces include potential public health risks related to increased vectors or hosts for

infectious diseases, toxic algal blooms, drowning and aeroallergens (Choi etal., 2021; Stewart-Sinclair etal., 2020; Chapter 6).

Climate adaptation and mitigation policies in the building sector offer multiple well-being and health co-beneﬁts (high conﬁdence)

(Diaz-Mendez etal., 2018; Macnaughton etal., 2018; Chpater 3 section 3.6.2). Leadership in Energy and Environmental Design (LEED)

certiﬁed buildings in the USA, Brazil, China, India, Germany and Turkey saved an estimated USD 7.5billion in energy costs and averted

33 Mt of CO

from 2000–2016 (Macnaughton etal., 2018). These measures can increase health beneﬁts through better indoor air quality,

reduction of the heat island effect, improved social well-being through energy poverty alleviation, creation of new jobs, increased

productive time and income, increased thermal comfort and lighting indoors and reduced noise impact (Smith etal., 2016; McCollum

etal., 2018; Thema etal., 2017; Mirasgedis etal., 2014; Alawneh etal., 2019; Diaz-Mendez etal., 2018). The value of these multiple co-

beneﬁts associated with climate actions in buildings is equal to or greater than the costs of energy savings (Ürge-Vorsatz etal., 2016;

Payne etal., 2015; Chapter 14 section 14.4.5).

Shifting to sustainable food systems that provide affordable, diverse and plant-rich diets with moderate quantities of GHG-intensive

animal protein can bring health co-beneﬁts and substantially reduce GHG emissions, especially in high income countries and where ill

health related to overconsumption of animal-based products is prevalent (very high conﬁdence) (Chapter 5 section 5.12.6; Chapter 7

section 7.4, Chapter 13 section 13.5; Springmann etal., 2018c; IPCC, 2019b; Clark and Tilman, 2017; Poore and Nemecek, 2018; Hayek

etal., 2021). Transforming the food system by limiting the demand for GHG-intensive animal foods, reducing food over-consumption and

transitioning to nutritious, plant-rich diets can have signiﬁcant co-beneﬁts to health (high conﬁdence) (Hedenus etal., 2014; Ripple etal.,

2014; Tirado, 2017; Springmann etal., 2018c; IPCC, 2018; IPCC, 2019a; IPCC, 2019b; Nelson etal., 2016; Willett etal., 2019; Tilman and

Clark, 2014; Green etal., 2015; Springmann etal., 2016b; Springmann etal., 2018b; Springmann etal., 2018a; Springmann etal., 2018c;

Milner etal., 2015; Milner etal., 2017; Farchi etal., 2017; Song etal., 2017; Willett etal., 2019). Reduction of red meat consumption

reduces the risk of cardiovascular disease (CVD) and colorectal cancer; the consumption of more fruits and vegetables can reduce the risk

of CVD, type II diabetes, cancer and all causes of mortality (Tilman and Clark, 2014; Sabate and Soret, 2014; Willett etal., 2019; Chapter

7 section 7.4; Chapter 5 section 5.12.5). Globally, it is estimated that transitioning to more plant-based diets—in line with World Health

Organization (WHO) recommendations on healthy eating—could reduce global mortality by 6‒10% and food-related GHG emissions by

29‒70% by 2050 (Springmann etal., 2016b). There are limitations in accessibility of affordable of healthy and diverse diets for all

(Springmann etal., 2020) and trade-offs such as the potential increase of GHG emissions from producing healthy and diverse diets in

low- and medium-income countries (Semba etal., 2020). Agroecological approaches have mitigation and adaptation potential and

deliver ecosystem services, biodiversity, livelihoods and beneﬁts to nutrition, health and equity (Rosenstock etal., 2019; Bezner Kerr etal.,

2021; Chapter 5 sections 5.4.4, 5.14.1; Chapter 13 section 13.5; Chapter 14 section 14.4.4).

Cross-Chapter BoxHEALTH (continued)

1126

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Frequently Asked Questions

FAQ 7.1 | How will climate change affect physical and mental health and well-being?

Climate change will affect human health and well-being in a variety of direct and indirect ways that depend on

exposure to hazards and vulnerabilities that are heterogeneous and vary within societies, and that are inﬂuenced

by social, economic and geographical factors and individual differences (see Figure FAQ7.1.1). Changes in the

magnitude, frequency and intensity of extreme climate events (e.g., storms, ﬂoods, wildﬁres, heatwaves and dust

storms) will expose people to increased risks of climate-sensitive illnesses and injuries and, in the worst cases, higher

mortality rates. Increased risks for mental health and well-being are associated with changes caused by the impacts

of climate change on climate-sensitive health outcomes and systems (see FigureFAQ7.1.2). Higher temperatures and

changing geographical and seasonal precipitation patterns will facilitate the spread of mosquito- and tick-borne

diseases, such as Lyme disease and dengue fever, and water- and food-borne diseases. An increase in the frequency

of extreme heat events will exacerbate health risks associated with cardiovascular disease and affect access to

freshwater in multiple regions, impairing agricultural productivity and increasing food insecurity, undernutrition

and poverty in low-income areas.

Pathways from hazards, exposure and vulnerabilities to climate change impacts on health outcomes

and health Systems

Vulnerability

Exposure

Hazard

Risk

Age

Gender

Mobility

Access to care

Socio-economic status

Pre-existing conditions

Characteristics of health system

Outdoor employment

Housing quality

Location/local geography

Livelihood type

Heat, drought

Floods

Storms

Vector spread

CLIMATE SENSITIVE OUTCOMES

- Adverse health (VBD, WBD, infectious disease,

heat-related illness, mental health, under nutrition)

- Migration & displacement

- Conflicts

SYSTEM IMPACTS

Health system (patient loads, emergency responses, costs)

- Food systems

- Livelihood systems

IMPACTS

FigureFAQ7.1.1 | Pathways from hazards, exposure and vulnerabilities to climate change impacts on health outcomes and health systems.

WBD: waterborne disease, VBD: Vector-borne disease, and FBD: Food-borne disease.

1127

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Climate change impacts on mental health and adaptation responses

Hazard

Acute events

(e.g. storms, floods, wildfires,

extreme heat)

Chronic changes

(e.g. drought, sea level rise,

sea ice loss, changing climate

normals)

Vulnerability

Pre-existing health conditions

Socio-economic inequities

Gender

Age

Occupation

Exposure

Direct exposure(s)

Indirect exposure(s)

(e.g. displacement, food systems

disruption, occupational loss)

Vicarious exposure(s)

(e.g. observed experiences of others,

media depictions of climate change)

Key adaptation responses

Scale of adaptation

Institutional

State and non-state actors:

effective mental health systems,

planning and preparedness, informed

policies, early intervention

Local governments:

planning, design, green infrastructure

Community

- Supportive social networks, effective

information channels

Individuals

- awareness, preparedness, mental

health support, nature-based therapy

Risks to mental health and wellbeing

Mental illness

[e.g., PTSD, depression, suicide]

Diminished wellbeing

[e.g., stress, climate anxiety, cognitive

impairment )

Diminished social relations

[e.g., loss of culture, interpersonal violence)

2. Vulnerability

Physiological factors

Social factors

3.Exposure

Direct exposure(s)

Indirect exposure(s)

Vicarious exposure(s)

4. Response

Institutional

Community

Individuals

1. Hazard

Acute events

Chronic changes

5. Risks

to mental health

and wellbeing

FigureFAQ7.1.2 | Climate change impacts on mental health and key adaptation responses.

PTSD: Post traumatic stress disorder.

Box FAQ7.1 (continued)

1128

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Frequently Asked Questions

FAQ 7.2 | Will climate change lead to wide-scale forced migration and involuntary displacement?

Climate change will have impacts on future migration patterns that will vary by region and over time, depending

on the types of climate risks people are exposed to, their vulnerability to those risks and their capacity—and the

capacity of their governments—to adapt and respond. Depending on the range of adaptation options available,

households may use migration as a strategy to adapt to climate risks, often through labour migration. The most

common drivers of climate-related displacement are extreme weather events, ﬂoods and droughts, especially when

these events cause severe damage to homes, livelihoods and food systems. Rising sea levels will present a new

risk for communities situated in low-lying coastal areas and small island states. The greater the scale of future

warming and extreme events, the greater the potential scale of future, involuntary climate-related migration and

displacement.Progress towards achieving the Sustainable Development Goals (SDGs) has strong potential to reduce

future involuntary climate-related migration and displacement.

Frequently Asked Questions

FAQ 7.4 | What solutions can effectively reduce climate change risks to health, well-being, forced migration and

conﬂict?

The solution space includes policies, strategies and programmes that consider why, how, when and who should be

involved to sustainably adapt to climate change. Effectively preparing for and managing the health risks of climate

change requires considering the multiple interacting sectors that affect population health and effective functioning

of health systems. Considering the close inter-connections between health, migration and conﬂict, interventions

that address climate risks in one area often have synergistic beneﬁts in others. For example, conﬂicts often result

in large numbers of people being involuntarily displaced and facilitate the spread of climate-sensitive diseases;

tackling the underlying causes of vulnerability and exposure that generate conﬂict reduces risks across all areas.

A key starting point for health and well-being is strengthening public health systems so that they become more

climate resilient, which also requires cooperation with other sectors (water, food, sanitation, transportation, etc.) to

ensure appropriate funding and progress on sustainable development goals. Interventions to enhance protection

against speciﬁc climate-sensitive health risks could reduce morbidity and mortality and prevent many losses and

damages (FigureFAQ7.4.1). These range from malaria net initiatives, vector control programmes, health hazard

(syndromic) surveillance and early warning systems, improving access to water, sanitation and hygiene (WASH), heat

action plans (HAPs), behavioural changes and integration with disaster risk reduction (DRR) and response strategies.

More importantly, climate resilient development pathways (CRDPs) are essential to improve overall health and

well-being, reduce underlying causes of vulnerability and provide a framework for prioritising mitigation and

Frequently Asked Questions

FAQ 7.3 | Will climate change increase the potential for violent conﬂict?

Climate hazards have affected armed conﬂict within countries but the observed inﬂuence of climate is small relative

to socioeconomic, political and cultural factors. Adverse impacts of climate change threaten to increase poverty

and inequality, undermine progress in meetings Sustainable Development Goals (SDGs) and place strain on civil

institutions—all of which are factors that contribute to the emergence or worsening of civil unrest and conﬂict.

Climate change impacts on crop productivity and water availability can function as a ‘risk multiplier’ for conﬂict in

areas that are already politically and/or socially fragile and, depending on circumstances, could increase the length

or the nature of an existing conﬂict. Institutional initiatives within or between states to protect the environment

and manage natural resources can serve simultaneously as mechanisms for engaging rival groups and adversaries

to cooperate in policymaking and peacebuilding.

1129

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Frequently Asked Questions

FAQ 7.5 | What are some speciﬁc examples of actions taken in other sectors that reduce climate change risks in the

health sector?

Many actions taken in other sectors to address the risks of climate change can lead to beneﬁts for health and

well-being. Adaptive urban design that provides greater access to green and natural spaces simultaneously enhances

biodiversity, improves air quality and moderates the hydrological cycle; it also helps reduce health risks associated

with heat stress and respiratory illnesses, and mitigates mental health challenges associated with congested

urban living. Transitioning away from internal-combustion vehicles and fossil fuel-powered generating stations

to renewable energy mitigates greenhouse gas emissions, improves air quality and lowers the risks of respiratory

illnesses. Policies and designs that facilitate active urban transport (walking and bicycling) increase efﬁciency in

that sector, reduce emissions, improve air quality and generate physical and mental health beneﬁts for residents.

Improved building and urban design that foster energy efﬁciency improve indoor air quality and reduce risks of

heat stress and respiratory illness. Food systems that emphasise healthy, plant-centred diets reduce emissions in the

agricultural sector while helping in the ﬁght against malnutrition.

adaptation options that support sustainable development. Transformative changes in key sectors including water,

food, energy, transportation and built environments offer signiﬁcant co-beneﬁts for health.

Adaptation responses to climatic risks

Adaptation in

other sectors

Vulnerability

Exposure

Hazard

Risk

Sustainable

livelihoods

Ch. 8

Biodiversity

Ch. 2; CCP 1

Ocean &

coastal systems

Ch. 3

Urban

systems

Ch. 6

Water & food

Ch. 4, 5

Adaptation responses

Can reduce vulnerability

Can reduce exposure

Health sector

adaptation

FigureFAQ7.4.1 | Solution space for adaptation to climate change in health and other sectors.

FAQ7.4 (continued)

1130

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Acknowledgements

The Lead Autors wish to recognize with thanks the research assistance

and editorial assistance provided by Alyssa Gatt and Edi Cadham of

Wilfrid Laurier University (Canada).

References

Alava, J. J., W. W. Cheung, P. S. Ross and, U. R. Sumaila, 2017: Climate change–

contaminant interactions in marine food webs: toward a conceptual

framework. Glob. Change Biol., 23, 3984–4001, doi:10.1111/gcb.13667.Call,

M. A., C. Gray, M. Yunus and M. Emch, 2017: Disruption, not displacement:

environmental variability and temporary migration in Bangladesh. Glob.

Environ. Change, 46, 157–165.

Abbott, M., M. Bazilian, D. Egel and H.H. Willis, 2017: Examining the food–

energy–water and conﬂict nexus. Curr. Opin. Chem. Eng., 18, 55–60.

Abdullah, A.S.M., etal., 2019: Effects of climate change and maternal morality:

perspective from case studies in rural Bangladesh. Int. J. Environ. Res. Public

Health, 16(23), 4594; https://doi.org/10.3390/ijerph16234594

Abel, D.W., etal., 2018: Air-quality-related health impacts from climate change

and from adaptation of cooling demand for buildings in the eastern United

States: an interdisciplinary modeling study. PLoS Med, 15(7), doi:10.1371/

journal.pmed.1002599.

Abel, G. and N. Sander, 2014: Quantifying global international migration ﬂows.

Science, 343(6178), 1520–1522, doi:10.1126/science.1248676.

Abel, G.J., B. Barakat, K.C. Samir and W. Lutz, 2016: Meeting the sustainable

development goals leads to lower world population growth. Proc. Natl.

Acad. Sci., 113(50), 14294–14299.

Abel, G.J., M. Brottrager, J.C. Cuaresma and R. Muttarak, 2019: Climate, conﬂict

and forced migration. Glob. Environ. Change, 54, 239–249.

Abhijith, K.V., et al., 2017: Air pollution abatement performances of green

infrastructure in open road and built-up street canyon environments – a

review. Atmos. Environ., 162, 71–86, doi:10.1016/j.atmosenv.2017.05.014.

Abiona, O., 2017: Adverse effects of early life extreme precipitation shocks on

short-term health and adulthood welfare outcomes. Rev. Dev. Econ., 21(4),

1229–1254.

Aboubakri, O., etal., 2020: Projection of mortality attributed to heat and cold;

the impact of climate change in a dry region of Iran, Kerman. Sci. Total

Environ., 728, 138700, doi:10.1016/j.scitotenv.2020.138700.

Abrahams, D., 2020: Conﬂict in abundance and peacebuilding in scarcity:

challenges and opportunities in addressing climate change and conﬂict.

World Dev., 132, 104998.

Abramson, M., etal., 2015: The resilience activation framework: a conceptual

model of how access to social resources promotes adaptation and rapid

recovery in post-disaster settings. J. Behav. Health Serv. Res., 42(1), 42–57.

Achakulwisut, P., etal., 2019: Effects of increasing aridity on ambient dust and

public health in the U.S. southwest under climate change. Geohealth, 3(5),

127–144, doi:10.1029/2019gh000187.

Achakulwisut, P., L.J. Mickley and S.C. Anenberg, 2018: Drought-sensitivity

of ﬁne dust in the US southwest: implications for air quality and public

health under future climate change. Environ. Res. Lett., 13(5), 54025,

doi:10.1088/1748-9326/aabf20.

Acharya, B.K., et al., 2018: Present and future of dengue fever in Nepal:

mapping climatic suitability by ecological niche model. Int. J. Environ. Res.

Public Health, 15(2), doi:10.3390/ijerph15020187.

Acharya, Y., et al., 2019: Nutritional status, cognitive achievement, and

educational attainment of children aged 8–11 in rural South India. PLoS

ONE, 14(10), doi:10.1371/journal.pone.0223001.

Achebak, H., D. Devolder, V. Ingole and J. Ballester, 2020: Reversal of the

seasonality of temperature-attributable mortality from respiratory diseases

in Spain. Nat. Commun., 11(1), doi:10.1038/s41467-020-16273-x.

Adams, C., T. Ide, J. Barnett and A. Detges, 2018: Sampling bias in climate-

conﬂict research. Nat. Clim. Change, 8(3), 200–203, doi:10.1038/s41558-

018-0068-2.

Adams, H., 2016: Why populations persist: mobility, place attachment and

climate change. Popul. Environ., 37(4), 429–448.

Adams, H. and S. Kay, 2019: Migration as a human affair: Integrating individual

stress thresholds into quantitative models of climate migration. Environ. Sci.

Policy, 93, 129–138, doi:10.1016/j.envsci.2018.10.015.

Adams, W.M. and J.F. Jardine, 2020: Overview of exertional heat illness. In:

Exertional Heat Illness: a Clinical and Evidence-Based Guide, pp. 1–16.

doi:10.1007/978-3-030-27805-2_1.

Adelodun, B., etal., 2020: Snowballing transmission of COVID-19 (SARS-CoV-2)

through wastewater: any sustainable preventive measures to curtail the

scourge in low-income countries? Sci. Total Environ., 742, doi:10.1016/j.

scitotenv.2020.140680.

Adelphi & Chemonics International 2020: Pathways to Peace: Addressing

Conﬂict and Strengthening Stability in a Changing Climate. Lessons

Learned from Resilience and Peacebuilding Programs in the Horn of Africa.

Adaptation Thought Leadership and Assessments (ATLAS), United States

Agency for International Development.

Adger, W., etal., 2015: Focus on environmental risks and migration: causes

and consequence. Environ. Res. Lett., 10, 60201, doi:10.1088/1748-

9326/10/6/060201.

Adhvaryu, A., J. Fenske and A. Nyshadham, 2019: Early life circumstance and

adult mental health. J. Polit. Econ., 127(4), 1516-1549.

Aﬁﬁ, T., et al., 2016: Human mobility in response to rainfall variability:

opportunities for migration as a successful adaptation strategy in eight case

studies. Migr. Dev., 5, 254–274.

Afriyie, K., J. Ganle and E. Santos, 2018: ‘The ﬂoods came and we lost

everything’: weather extremes and households’ asset vulnerability and

adaptation in rural Ghana. Clim. Dev., 10(3), 259–274, doi:10.1080/1756

5529.2017.1291403.

Afshin, A., etal., 2019: Health effects of dietary risks in 195countries, 1990–

2017: a systematic analysis for the Global Burden of Disease Study 2017.

Lancet, 393(10184), 1958–1972, doi:10.1016/S0140-6736(19)30041-8.

Ahmad, S., S. Pachauri and F. Creutzig, 2017: Synergies and trade-offs

between energy-efﬁcient urbanization and health. Env. Res. Lett., 12(11),

doi:10.1088/1748-9326/aa9281.

Ahmadalipour, A. and H. Moradkhani, 2018: Escalating heat-stress mortality

risk due to global warming in the Middle East and North Africa (MENA).

Environ. Int., 117, 215–225, doi:10.1016/j.envint.2018.05.014.

Ahmed, K.J., S.M. A. Haq and F. Bartiaux, 2019: The nexus between extreme

weather events, sexual violence, and early marriage: a study of vulnerable

populations in Bangladesh. Popul. Environ., 40(3), 303–324.

Ahmed, S., etal., 2020: Effect of extreme weather events on injury, disability,

and death in Bangladesh. Clim Dev, 13(4), 306-317.

Ahmed, S.A., M. Guerrero Flórez and P. Karanis, 2018: The impact of water

crises and climate changes on the transmission of protozoan parasites in

Africa. Pathog. Glob. Health, 112(6), 281–293.

1131

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Ahmed, S.A., A. Saad-Hussein, A. El Feel and M. A. Hamed, 2014: Time series

trend of bilharzial bladder cancer in Egypt and its relation to climate change:

a study from 1995–2005. Int. J. Pharm. Clin. Res., 6(1), 46–53.

Aik, J., et al., 2018: Climate variability and salmonellosis in Singapore – a

time series analysis. Sci. Total Environ., 639, 1261–1267, doi:10.1016/j.

scitotenv.2018.05.254.

Aitsi-Selmi, A. and V. Murray, 2016: Protecting the health and well-being of

populations from disasters: health and health care in the Sendai framework

for disaster risk reduction 2015–2030. Prehosp. Disaster Med., 31(1), 74–78,

doi:10.1017/S1049023X15005531.

Aitsi-Selmi, A., etal., 2015: The Sendai Framework for disaster risk reduction:

renewing the global commitment to people’s resilience, health, and well-

being. Int. J. Disaster Risk Sci., 6(2), 164–176, doi:10.1007/s13753-015-

0050-9.

Ajibade, I., M. Sullivan and M. Haeffner, 2020: Why climate migration is not

managed retreat: six justiﬁcations. Glob. Environ. Change, 65, 102187.

Akande, V., T. Fawehinmi, R. Ruiter and S. Kremers, 2021: Healthy dietary choices

and physical activity participation in the Canadian arctic: understanding

Nunavut Inuit perspectives on the barriers and enablers. Int. J. Environ. Res.

Public Health, 18(3), doi:10.3390/ijerph18030940.

Akil, L., H.A. Ahmad and R.S. Reddy, 2014: Effects of climate change

on Salmonella infections. Foodborne Pathog. Dis., 11(12), 974–980,

doi:10.1089/fpd.2014.1802.

Akompab, D., et al., 2013: Heat waves and climate change: applying the

health belief model to identify predictors of risk perception and adaptive

behaviours in Adelaide, Australia. Int. J. Environ. Res. Public Health, 10(6),

2164–2184, doi:10.3390/ijerph10062164.

Akpan, G., K. Adepoju, O. Oladosu and S. Adelabu, 2018: Dominant malaria

vector species in Nigeria: modelling potential distribution of Anopheles

gambiae sensu lato and its siblings with MaxEnt. PLoS ONE, 13(10),

doi:10.1371/journal.pone.0204233.

Akter, R., etal., 2017: Joint effects of climate variability and socioecological

factors on dengue transmission: epidemiological evidence. Trop. Med. Int.

Health, 22(6), 656–669, doi:10.1111/tmi.12868.

Al-Bouwarthan, M., M.M. Quinn, D. Kriebel and D.H. Wegman, 2020: Risk

of kidney injury among construction workers exposed to heat stress: a

longitudinal study from Saudi Arabia. Int. J. Environ. Res. Public Health,

17(11), doi:10.3390/ijerph17113775.

Alawneh, R., F. Ghazali, H. Ali and M. Asif, 2019: A new index for assessing the

contribution of energy efﬁciency in LEED 2009 certiﬁed green buildings to

achieving UN sustainable development goals in Jordan. Int. J. Green Energy,

16(6), 490–499, doi:10.1080/15435075.2019.1584104.

Albert, S., et al., 2018: Heading for the hills: climate-driven community

relocations in the Solomon Islands and Alaska provide insight for a 1.5°C

future. Reg. Environ. Change, 2261–2272, doi:10.1007/s10113-017-1256-8.

Albrecht, G., etal., 2007: Solastalgia: the distress caused by environmental change.

Australas. Psychiatry, 15, S95–S98, doi:10.1080/10398560701701288.

Alderman, H., 2016: Leveraging Social Protection Programs for Improved

Nutrition: Summary of Evidence Prepared for the Global Forum on Nutrition-

Sensitive Social Protection Programs, 2015. World Bank, Washington, DC.

Aleksandrova, M., 2020: Principles and considerations for mainstreaming

climate change risk into national social protection frameworks in developing

countries. Clim. Dev., 12(6), 511–520, doi:10.1080/17565529.2019.1642180.

Alele, F., B. Malau-Aduli, A. Malau-Aduli and M. Crowe, 2020: Systematic review

of gender differences in the epidemiology and risk factors of exertional heat

illness and heat tolerance in the armed forces. BMJ Open, 10(4), doi:10.1136/

bmjopen-2019-031825.

Alexander, P.J., R. Fealy and G.M. Mills, 2016: Simulating the impact of urban

development pathways on the local climate: a scenario-based analysis

in the greater Dublin region, Ireland. Landsc. Urban Plan., 152, 72–89,

doi:10.1016/j.landurbplan.2016.02.006.

Alfredsson, E., etal., 2018: Why achieving the Paris Agreement requires reduced

overall consumption and production. Sustain. Sci. Pract. Policy, 14(1), 1–5.

Ali, S., etal., 2020: Mental health in the Paciﬁc: urgency and opportunity. Asia

Pac. Viewp., doi:10.1111/apv.12286.

Almendra, R., et al., 2019: Short-term impacts of air temperature on

hospitalizations for mental disorders in Lisbon. Sci. Total Environ., 647, 127–

133, doi:10.1016/j.scitotenv.2018.07.337.

Alpino, T.A., A.R.M. d. Sena and C.M. d. Freitas, 2016: Desastres relacionados

à seca e saúde coletiva – uma revisão da literatura cientíﬁca. Ciên. Saúde

Colet., 21(3), 809–820, doi:10.1590/1413-81232015213.21392015.

Alston, M. and B. Akhter, 2016: Gender and food security in Bangladesh: the

impact of climate change. Gend. Place Cult., 23(10), 1450–1464, doi:10.108

0/0966369x.2016.1204997.

Altieri, M., C. Nicholls, A. Henao and M. Lana, 2015: Agroecology and the

design of climate change-resilient farming systems. Agron. Sustain. Dev.,

35(3), 869–890, doi:10.1007/s13593-015-0285-2.

Amaratunga, D., etal., 2018: Sound practices of disaster risk reduction at local

level. Procedia Eng., 212, 1163–1170, doi:10.1016/j.proeng.2018.01.150.

Amat, F., etal., 2020: Pandemics meet democracy: experimental evidence from

the COVID-19 crisis in Spain. SOC Arxiv Pap., doi:10.31235/osf.io/dkusw.

Amegah, A.K., G. Rezza and J.J. Jaakkola, 2016: Temperature-related morbidity

and mortality in Sub-Saharan Africa: a systematic review of the empirical

evidence. Environ. Int., 91, 133–149, doi:10.1016/j.envint.2016.02.027.

Amstislavski, P., etal., 2013: Effects of increase in temperature and open water

on transmigration and access to health care by the Nenets reindeer herders

in northern Russia. Int. J. Circumpolar Health, 72, 447–455, doi:10.3402/

ijch.v72i0.21183.

An, R., M. Ji and S. Zhang, 2018: Global warming and obesity: a systematic

review. Obes. Rev., 19(2), 150–163, doi:10.1111/obr.12624.

Analitis, A., etal., 2014: Effects of heat waves on mortality effect modiﬁcation

and confounding by air pollutants. Epidemiology, 25(1), 15–22, doi:10.1097/

EDE.0b013e31828ac01b.

Andersen, L.K. and M.D.P. Davis, 2017: A wake-up call to dermatologists

– climate change affects the skin. Int. J. Dermatol., 56(10), e198–e199,

doi:10.1111/ijd.13617.

Anderson, G.B., K.W. Oleson, B. Jones and R.D. Peng, 2018: Projected trends in

high-mortality heatwaves under different scenarios of climate, population,

and adaptation in 82 US communities. Clim. Change, 146(3-4), 455–470,

doi:10.1007/s10584-016-1779-x.

Andrews, O., et al., 2018: Implications for workability and survivability in

populations exposed to extreme heat under climate change: a modelling study.

Lancet Planet. Health, 2(12), E540–E547, doi:10.1016/s2542-5196(18)30240-7.

Anenberg, S.C., et al., 2020: Synergistic health effects of air pollution,

temperature, and pollen exposure: a systematic review of epidemiological

evidence. Environ. Health, 19(1), doi:10.1186/s12940-020-00681-z.

Anwar, N., A. Sawas and D. Mustafa, 2019: ‘Without water, there is no life’:

negotiating everyday risks and gendered insecurities in Karachi’s informal

settlements. Urban Stud., doi:10.1177/0042098019834160.

Apergis, N., 2018: The impact of greenhouse gas emissions on personal well-

being: evidence from a panel of 58countries and aggregate and regional

country samples. J. Happiness Stud., 19(1), 69–80, doi:10.1007/s10902-016-

9809-y.

Aracena, S., etal., 2021: Health system adaptation to climate change: a Peruvian

case study. Health Policy Plan., 36(1), 45–83, doi:10.1093/heapol/czaa072.

Arbuthnott, K., S. Hajat, C. Heaviside and S. Vardoulakis, 2020: Years of life lost

and mortality due to heat and cold in the three largest English cities. Environ.

Int., 144, 105966, doi:10.1016/j.envint.2020.105966.

Archer, J., et al., 2020: Intestinal schistosomiasis and giardiasis co-infection

in Sub-Saharan Africa: Can a one health approach improve control of

each waterborne parasite simultaneously? Trop. Med. Infect. Dis., 5(3),

doi:10.3390/tropicalmed5030137.

Archibald, A.T., etal., 2020: On the changes in surface ozone over the twenty-

ﬁrst century: sensitivity to changes in surface temperature and chemical

mechanisms. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci., 378(2183),

doi:10.1098/rsta.2019.0329.

1132

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik,

M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W.

Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala,

R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D.

Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca,

A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V.

Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett,

L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt,

P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S.

Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K.

Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R.

Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath,

S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van

den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld,

2021: Technical Summary. In Climate Change 2021: The Physical Science

Basis. Contribution of Working Group I to the Sixth Assessment Report of the

Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A.

Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis,

M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterﬁeld, O.

Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.

Armitage, R. and L. Nellums, 2020: Water, climate change, and COVID-19:

prioritising those in water-stressed settings. Lancet Planet. Health, 4(5),

E175–E175.

Armstrong, B., etal., 2017: Longer-term impact of high and low temperature on

mortality: an international study to clarify length of mortality displacement.

Environ. Health Perspect., 125(10), doi:10.1289/ehp1756.

Arnall, A., 2014: A climate of control: ﬂooding, displacement and planned

resettlement in the Lower Zambezi River valley, Mozambique. Geogr. J.,

180(2), 141–150, doi:10.1111/geoj.12036.

Arora, D., etal., 2017: Gender (Im)balance in Productive and Reproductive Labor

Among Livestock Producers in Colombia: Implications for Climate Change

Responses. CGIAR Research Program on Climate Change, Agriculture

and Food Security, https://hdl.handle.net/10568/79940 Last accessed 30

September 2021.

Arroyo, V., etal., 2016: Impact of air pollution and temperature on adverse

birth outcomes: Madrid, 2001–2009. Environ. Pollut., 218, 1154–1161,

doi:10.1016/j.envpol.2016.08.069.

Asadgol, Z., etal., 2019: The effect of climate change on cholera disease: the

road ahead using artiﬁcial neural network. PLoS ONE, 14(11), e224813,

doi:10.1371/journal.pone.0224813.

Aseﬁ-Najafabady, S., et al., 2018: Climate change, population, and poverty:

vulnerability and exposure to heat stress in countries bordering the Great

Lakes of Africa. Clim. Change, 148(4), 561–573, doi:10.1007/s10584-018-

2211-5.

Ash, K. and N. Obradovich, 2020: Climatic stress, internal migration, and Syrian

civil war onset. J. Conﬂ. Resolut., 64(1), 3–31.

Asmall, T., etal., 2021: The adverse health effects associated with drought in

Africa. Sci. Total Environ., 793, 148500, doi:10.1016/j.scitotenv.2021.148500.

Asmare, B., M. Taddele, S. Berihun and F. Wagnew, 2018: Nutritional status

and correlation with academic performance among primary school children,

northwest Ethiopia. BMC Res. Notes, 11(1), 1–6.

Asplund, T. and M. Hjerpe, 2020: Project coordinators’ views on climate

adaptation costs and beneﬁts–justice implications. Local Environ., 25(2),

114–129.

Åström, C., etal., 2017: Vulnerability reduction needed to maintain current

burdens of heat-related mortality in a changing climate—magnitude and

determinants. Int. J. Environ. Res. Public Health, 14(7), 741–741.

Åström, D.O., etal., 2020: Heat wave-related mortality in Sweden: a case-crossover

study investigating effect modiﬁcation by neighbourhood deprivation.

Scand. J. Public Health, 48(4), 428–435, doi:10.1177/1403494818801615.

Åström, D.O., K.L. Ebi, A.M. Vicedo-Cabrera and A. Gasparrini, 2018: Investigating

changes in mortality attributable to heat and cold in Stockholm, Sweden. Int.

J. Biometeorol., 62(9), 1777–1780, doi:10.1007/s00484-018-1556-9.

Åström, D.O., B. Forsberg, K.L. Ebi and J. Rocklöv, 2013: Attributing mortality

from extreme temperatures to climate change in Stockholm, Sweden. Nat.

Clim. Change, 3(12), 1050–1054, doi:10.1038/nclimate2022.

Asugeni, J., D. MacLaren, P.D. Massey and R. Speare, 2015: Mental health

issues from rising sea level in a remote coastal region of the Solomon

Islands: current and future. Australas. Psychiatry, 23(6), 22–25,

doi:10.1177/1039856215609767.

Atef, S.S., F. Sadeqinazhad, F. Farjaad and D.M. Amatya, 2019: Water conﬂict

management and cooperation between Afghanistan and Pakistan. J. Hydrol.

Reg. Stud., 570, 875–892.

Atkinson, H. and J. Bruce, 2015: Adolescent girls, human rights and the

expanding climate emergency. Ann. Glob. Health, 81(3), 323–330,

doi:10.1016/j.aogh.2015.08.003.

Åtland, K., 2013: The security implications of climate change in the Arctic

Ocean. In: Environmental Security in the Arctic Ocean. Springer, Dordrecht.

pp. 205–216.

Auger, N., etal., 2017: Elevated outdoor temperatures and risk of stillbirth. Int.

J. Epidemiol., 46(1), 200–208.

Avaria-Llautureo, J., etal., 2021: Historical warming consistently decreased size,

dispersal and speciation rate of ﬁsh. Nat. Clim. Change, 1–7. DOI https://doi.

org/10.1038/s41558-021-01123-5.

Avashia, V., A. Garg and H. Dholakia, 2021: Understanding temperature related

health risk in context of urban land use changes. Landsc. Urban Plan., 212,

doi:10.1016/j.lurbplan.2021.104107.

Avila-Palencia, I., et al., 2018: The effects of transport mode use on self-

perceived health, mental health, and social contact measures: a cross-

sectional and longitudinal study. Environ. Int., 120, 199–206, doi:10.1016/j.

envint.2018.08.002.

Awuor, L., R. Meldrum and E. Liberda, 2020: Institutional engagement practices

as barriers to public health capacity in climate change policy discourse:

lessons from the Canadian province of Ontario. Int. J. Environ. Res. Public

Health, 17(17), doi:10.3390/ijerph17176338.

Axelsen, J.B., R. Yaari, B.T. Grenfell and L. Stone, 2014: Multiannual forecasting of

seasonal inﬂuenza dynamics reveals climatic and evolutionary drivers. Proc.

Natl. Acad. Sci. U.S.A., 111(26), 9538–9542, doi:10.1073/pnas.1321656111.

Ayano, G., 2018: Signiﬁcance of mental health legislation for successful primary

care for mental health and community mental health services: a review. Afr.

J. Prim. Health Care Fam. Med., 10(1), 1–4, doi:10.4102/phcfm.v10i1.1429.

Ayeb-Karlsson, S., D. Kniveton and T. Cannon, 2020: Trapped in the prison of

the mind: notions of climate-induced (im)mobility decision-making and

wellbeing from an urban informal settlement in Bangladesh. Palgrave

Commun., 6(1), 62, doi:10.1057/s41599-020-0443-2.

Ayeb-Karlsson, S., C.D. Smith and D. Kniveton, 2018: A discursive review of the

textual use of ‘trapped’ in environmental migration studies: the conceptual

birth and troubled teenage years of trapped populations. Ambio, 47(5),

557–573, doi:10.1007/s13280-017-1007-6.

Azage, M., A. Kumie, A. Worku and A. Bagtzoglou, 2015: Childhood diarrhea

exhibits spatiotemporal variation in Northwest Ethiopia: a SaTScan spatial

statistical analysis. PLoS ONE, 10(12), doi:10.1371/journal.pone.0144690.

Baez, J., G. Caruso, V. Mueller and C. Niu, 2017: Droughts augment youth

migration in Northern Latin America and the Caribbean. Clim. Change,

140(3), 423–435.

Bai, X., etal., 2019: Distribution of geographical scale, data aggregation unit

and period in the correlation analysis between temperature and incidence of

HFRS in mainland China: A systematic review of 27 ecological studies. PLoS

Negl. Trop. Dis., 13(8), doi:10.1371/journal.pntd.0007688.

Bai, Y.T., etal., 2015: Effects of climate and rodent factors on hemorrhagic fever

with renal syndrome in Chongqing, China, 1997–2008. PLoS ONE, 10(7),

doi:10.1371/journal.pone.0133218.

Baidya, D., S. Maitra and S. Bhattacharjee, 2020: Facing post-cyclone disaster

in times of COVID-19 pandemic in India: possible testing strategy to reduce

further spread of disease. Asia-Pac. J. Public Health, 32(6-7), 376–376,

doi:10.1177/1010539520944717.

1133

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Baker-Austin, C., J. Trinanes, N. Gonzalez-Escalona and J. Martinez-Urtaza,

2017: Non-cholera vibrios: the microbial barometer of climate change.

Trends Microbiol., 25(1), 76–84, doi:10.1016/j.tim.2016.09.008.

Balestri, S. and M. A. Maggioni, 2017: Land-use change and communal

conﬂlicts in Sub-Saharan Africa. Peace Econ. Peace Sci. Public Policy, 23(4),

doi:10.1515/peps-2017-0032.

Bamberg, S., T. Masson, K. Brewitt and N. Nemetschek, 2017: Threat, coping

and ﬂood prevention – a meta-analysis. J. Environ. Psychol., 54, 116–126,

doi:10.1016/j.jenvp.2017.08.001.

Bandura, A., 1982: Self-efﬁcacy mechanism in human agency. American

Psychologist, 37(2), 122–147, doi:10.1037/0003-066X.37.2.122.

Bandyopadhyay, R., et al., 2016: Biological control of aﬂatoxins in Africa:

current status and potential challenges in the face of climate change. World

Mycotoxin J., 9(5), 771–789, doi:10.3920/wmj2016.2130.

Banerjee, S., R. Black, A. Mishra and D. Kniveton, 2019a: Assessing vulnerability

of remittance-recipient and nonrecipient households in rural communities

affected by extreme weather events: case studies from South-West China

and North-East India. Popul. Space Place, 25(2), doi:10.1002/psp.2157.

Banerjee, S., A. Hussain, S. Tuladhar and A. Mishra, 2019b: Building capacities

of women for climate change adaptation: insights from migrant-sending

households in Nepal. Clim. Change, 157(3), 587–609, doi:10.1007/s10584-

019-02572-w.

Bannister, T., etal., 2021: A pilot forecasting system for epidemic thunderstorm

asthma in southeastern Australia. Bull. Am. Meteorol. Soc., 102(2), E399–

E420, doi:10.1175/bams-d-19-0140.1.

Banwell, N., S. Rutherford, B. Mackey and C. Chu, 2018: Towards improved

linkage of disaster risk reduction and climate change adaptation in health: a

review. Int. J. Environ. Res. Public Health, 15(4), doi:10.3390/ijerph15040793.

Bao, J., et al., 2019: Effects of heat on ﬁrst-ever strokes and the effect

modiﬁcation of atmospheric pressure: a time-series study in Shenzhen, China.

Sci. Total Environ., 654, 1372–1378, doi:10.1016/j.scitotenv.2018.11.101.

Barach, P., etal., 2020: Disruption of healthcare: Will the COVID pandemic

worsen non-COVID outcomes and disease outbreaks? Prog. Pediatr. Cardiol.,

59, 101254, doi:10.1016/j.ppedcard.2020.101254.

Barange, M., et al., 2018: Impacts of Climate Change on Fisheries and

Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation

Options. Fisheries and Aquaculture Technical Paper. FAO, Rome.

Barnes, J.H., E.T. Hayes, T.J. Chatterton and J.W.S. Longhurst, 2014: Air

quality action planning: why do barriers to remediation in local air quality

management remain? J. Environ. Plan. Manag., 57(5), 660–681, doi:10.108

0/09640568.2012.762573.

Barnett, J., 2019: Global environmental change I: climate resilient peace? Prog.

Hum. Geogr., 43(5), 927–936, doi:10.1177/0309132518798077.

Barnett, J. and C. McMichael, 2018: The effects of climate change on the

geography and timing of human mobility. Popul. Environ., 39(4), 339–356,

doi:10.1007/s11111-018-0295-5.

Barquet, K., 2015: “Yes to peace”? Environmental peacemaking and

transboundary conservation in Central America. Geoforum, 63, 14–24.

Barquet, K., P. Lujala and J. Rod, 2014: Transboundary conservation and

militarized interstate disputes. Polit. Geogr., 42, 1–11, doi:10.1016/j.

polgeo.2014.05.003.

Barreca, A., etal., 2016: Adapting to climate change: the remarkable decline

in the US temperature-mortality relationship over the twentieth century. J.

Polit. Econ., 124(1), 105–159, doi:10.1086/684582.

Baryshnikova, N. and N. Pham, 2019: Natural disasters and mental

health: a quantile approach. Econ. Lett., 180, 62–66, doi:10.1016/j.

econlet.2019.04.016.

Basel, B., G. Goby and J. Johnson, 2020: Community-based adaptation to

climate change in villages of Western Province, Solomon Islands. Mar. Pollut.

Bull., 156, doi:10.1016/j.marpolbul.2020.111266.

Bateman, I., etal., 2020: Editorial: economics of the environment in the shadow

of coronavirus. Environ. Resour. Econ., 76(4), 519–523, doi:10.1007/s10640-

020-00496-z.

Battilani, P., et al., 2016: Aﬂatoxin B1 contamination in maize in Europe

increases due to climate change. Sci. Rep., 6. DOI: https://doi.org/10.1038/

srep24328

Baylis, P., etal., 2018: Weather impacts expressed sentiment. PLoS ONE, 13(4),

doi:10.1371/journal.pone.0195750.

Beach, R., etal., 2019: Combining the effects of increased atmospheric carbon

dioxide on protein, iron, and zinc availability and projected climate change

on global diets: a modelling study. Lancet Planet. Health, 3(7), E307–E317,

doi:10.1016/s2542-5196(19)30094-4.

Becquart, N.A., E.N. Naumova, G. Singh and K.K.H. Chui, 2018: Cardiovascular

disease hospitalizations in Louisiana parishes’ elderly before, during and

after Hurricane Katrina. Int. J. Environ. Res. Public Health, 16(1), doi:10.3390/

ijerph16010074.

Beggs, P.J., 2021: Climate change, aeroallergens, and the aeroexposome.

Environ. Res. Lett., 16(3), doi:10.1088/1748-9326/abda6 f.

Belay, A., J. Recha, T. Woldeamanuel and J. Morton, 2017: Smallholder farmers’

adaptation to climate change and determinants of their adaptation

decisions in the Central Rift Valley of Ethiopia. Agric. Food Secur., 6, 1–13,

doi:10.1186/s40066-017-0100-1.

Belesova, K., I. Kelman and R. Boyd, 2016: Governance through economic

paradigms: addressing climate change by accounting for health. Polit. Gov.,

4(4), 87–96, doi:10.17645/pag.v4i4.729.

Belkin, L. and M. Kouchaki, 2017: Too hot to help! Exploring the impact of

ambient temperature on helping. Eur. J. Soc. Psychol., 47(5), 525–538,

doi:10.1002/ejsp.2242.

Bellemare, M.F., 2015: Rising food prices, food price volatility, and social unrest.

Am. J. Agric. Econ., 97(1), 1–21.

Benmarhnia, T., etal., 2016: A difference-in-differences approach to assess the

effect of a heat action plan on heat-related mortality, and differences in

effectiveness according to sex, age, and socioeconomic status (Montreal,

Quebec). Environ. Health Perspect., 124(11), 1694–1699, doi:10.1289/

ehp203.

Benmarhnia, T., S. Deguen, J.S. Kaufman and A. Smargiassi, 2015: Vulnerability

to heat-related mortality a systematic review, meta-analysis, and

meta-regression analysis. Epidemiology, 26(6), 781–793, doi:10.1097/

ede.0000000000000375.

Bennett, S., et al., 2020: Understanding the implications of the Sustainable

Development Goals for health policy and systems research: results of a

research priority setting exercise. Global. Health, 16(1), doi:10.1186/s12992-

019-0534-2.

Benveniste, H., J.C. Cuaresma, M. Gidden and R. Muttarak, 2021: Tracing

international migration in projections of income and inequality across the

Shared Socioeconomic Pathways. Clim. Change, 166, doi:10.1007/s10584-

021-03133-w.

Benveniste, H., O. Michael and F. Marc, 2020: Effect of border policy on

exposure and vulnerability to climate change. Proc. Natl. Acad. Sci., 117(43),

doi:10.1073/pnas.2007597117.

Berndtsson, R. and K. Tussupova, 2020: The Future of Water Management in

Central Asia. Multidisciplinary Digital Publishing Institute, Basel, Switzerland.

Berrang-Ford, L., etal., 2021: Systematic mapping of global research on climate

and health: a machine learning review. Lancet Planet. Health. DOI: https://

doi.org/10.1016/S2542-5196(21)00179-0

Berry, H.L., K. Bowen and T. Kjellstrom, 2010: Climate change and mental

health: a causal pathways framework. Int. J. Public Health, 55(2), 123–132,

doi:10.1007/s00038-009-0112-0.

Berry, L. and D. Peel, 2015: Worrying about climate change: is it responsible to

promote public debate? BJPsych Int., 12(2), 31–32.

Berry, P., etal., 2018: Assessing health vulnerabilities and adaptation to climate

change: a review of international progress. Int. J. Environ. Res. Public Health,

15(12), doi:10.3390/ijerph15122626.

Bertana, A., 2020: The role of power in community participation: relocation

as climate change adaptation in Fiji. Environ. Plan. C Polit. Space, 38,

239965442090939, doi:10.1177/2399654420909394.

1134

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Bettini, G. and G. Gioli, 2016: Waltz with development: insights on the

developmentalization of climate-induced migration. Migr. Dev., 5(2), 171–

189.

Betts, R., etal., 2018: Changes in climate extremes, fresh water availability and

vulnerability to food insecurity projected at 1.5degrees C and 2degrees C

global warming with a higher-resolution global climate model. Philos. Trans.

Royal Soc. A Math. Phys. Eng. Sci., 376(2119), doi:10.1098/rsta.2016.0452.

Beveridge, L., et al., 2019: Experiences and drivers of food insecurity in

Guatemala’s dry corridor: insights from the integration of ethnographic

and household survey data. Front. Sustain. Food Syst., 3, doi:10.3389/

fsufs.2019.00065.

Beyene, T., D. Endalamaw, Y. Tolossa and A. Feyisa, 2015: Evaluation of rational

use of veterinary drugs especially antimicrobials and anthelmintics in

Bishoftu, Central Ethiopia. BMC Res. Notes. DOI: https://doi.org/10.1186/

s13104-015-1466-4

Beyer, J. and A. Vandermosten, 2021: Greenness of Stimulus Index. An

Assessment of COVID-19 Stimulus by G20 Countries and Other Major

Economies in Relation to Climate Action and Biodiversity Goals. Vivid

Economics, London, UK. 93 pp.

Bezner Kerr, R., et al., 2021: Can agroecology improve food security and

nutrition? A review. Glob. Food Secur. Policy Econ. Environ., 29, doi:10.1016/j.

gfs.2021.100540.

Bhagat, R., 2017: Migration, gender and right to the city. Econ. Polit. Wkly.,

volume LII .pp. 35–40.

Bharara, T., et al., 2018: Green hospital initiative by a North Delhi tertiary

care hospital: current scenario and future prospects. J. Clin. Diagn. Res., 12,

DC10–DC14, doi:10.7860/jcdr/2018/34360.11758.

Bhatt, S., etal., 2013: The global distribution and burden of dengue. Nature,

496(7446), 504–507, doi:10.1038/nature12060.

Bhatta, G., P. Aggarwal, S. Poudel and D. Belgrave, 2015: Climate-induced

migration in South Asia: migration decisions and the gender dimensions of

adverse climatic events. J. Rural Community Dev., 10, 1–23.

Bhavnani, R.R. and B. Lacina, 2015: The effects of weather-induced migration

on sons of the soil riots in India. World Pol., 67, 760.

Billiot, S., et al., 2019: Indigenous perspectives for strengthening social

responses to global environmental changes: a response to the social work

grand challenge on environmental change. J. Community Pract., 27(3-4),

296–316, doi:10.1080/10705422.2019.1658677.

Binder, S., J. Barile, C. Baker and B. Kulp, 2019: Home buyouts and household

recovery: neighborhood differences three years after Hurricane Sandy.

Environ. Hazards, 18(2), 127–145, doi:10.1080/17477891.2018.1511404.

Blattman, C. and E. Miguel, 2010: Civil war. J. Econ. Lit., 48(1), 3–57.

Bless, P.J., etal., 2016: Acute gastroenteritis and campylobacteriosis in Swiss

primary care: the viewpoint of general practitioners. PLoS ONE, 11(9),

e161650, doi:10.1371/journal.pone.0161650.

Boas, I., etal., 2019: Climate migration myths. Nat. Clim. Change, 9(12), 901–

903, doi:10.1038/s41558-019-0633-3.

Boas, I. and D. Rothe, 2016: From conﬂict to resilience? Explaining recent

changes in climate security discourse and practice. Environ. Polit., 25(4),

613–632, doi:10.1080/09644016.2016.1160479.

Bob, U. and S. Bronkhorst, 2014: Conﬂict-sensitive Adaptation to Climate

Change in Africa [Carius, A. and D. Tänzler (eds.)]. Climate Diplomacy Series.

Berliner Wissenschafts-Verlag, Berlin.

Boeckmann, M. and I. Rohn, 2014: Is planned adaptation to heat reducing

heat-related mortality and illness? A systematic review. BMC Public Health,

14, doi:10.1186/1471-2458-14-1112.

Bogdanova, E., etal., 2021: The impact of climate change on the food (in)

security of the Siberian indigenous peoples in the Arctic: environmental and

health risks. Sustainability, 13(5), doi:10.3390/su13052561.

Bohra-Mishra, P., etal., 2017: Climate variability and migration in the Philippines.

Popul. Environ., 38(3), 286–308, doi:10.1007/s11111-016-0263-x.

Boin, A., P. ’t Hart, E. Stern and B. Sundelius, 2005: The Politics of Crisis

Management: Public Leadership Under Pressure. Cambridge University

Press, Cambridge.

Boithias, L., etal., 2016: Hydrological regime and water shortage as drivers

of the seasonal incidence of diarrheal diseases in a tropical montane

environment. PLoS Negl. Trop. Dis., 10(12), e5195, doi:10.1371/journal.

pntd.0005195.

Bol, D., M. Giani, A. Blais and P. Loewen, 2020: The effect of COVID-19 lockdowns

on political support: some good news for democracy? Eur. J. Polit. Res., 60(2),

497–505, doi:10.1111/1475-6765.12401.

Bollyky, T.J., T. Templin, M. Cohen and J.L. Dieleman, 2017: Lower-income

countries that face the most rapid shift in noncommunicable disease burden

are also the least prepared. Health Aff., 36(11), 1866–1875.

Boon, E.K. and S.W. Anuga, 2020: Circular economy and its relevance for

improving food and nutrition security in Sub-Saharan Africa: the case of

Ghana. Mater. Circ. Econ., 2. pp. 1-14.

Boonruksa, P., T. Maturachon, P. Kongtip and S. Woskie, 2020: Heat

stress, physiological response, and heat-related symptoms among Thai

sugarcane workers. Int. J. Environ. Res. Public Health, 17(17), doi:10.3390/

ijerph17176363.

Booth, S. and D. Zeller, 2005: Mercury, food webs, and marine mammals:

implications of diet and climate change for human health. Environ. Health

Perspect., 113(5), 521–526, doi:10.1289/ehp.7603.

Borderon, M., etal., 2019: Migration inﬂuenced by environmental change in

Africa: a systematic review of empirical evidence. Demogr. Res., 41, 491–

544, doi:10.4054/DemRes.2019.41.18.

Borg, M., et al., 2017: The impact of daily temperature on renal disease

incidence: an ecological study. Environ. Health, 16, doi:10.1186/s12940-

017-0331-4.

Borg, M. A., etal., 2021: Occupational heat stress and economic burden: a review

of global evidence. Environ. Res., 195, doi:10.1016/j.envres.2021.110781.

Borrows, J., 2019: Earth bound: indigenous resurgence and environmental

reconciliation. In: Resurgence and Reconciliation: Indigenous-Settler

Relations and Earth Teachings [Asch, M., J. Borrows and J. Tully(eds.)].

University of Toronto Press, Toronto, pp. 49–82.

Boscarino, J.A., et al., 2017: Mental health outcomes among vulnerable

residents after Hurricane Sandy: implications for disaster research and

planning. Am. J. Disaster Med., 9(2), 97–106.

Boscarino, J.A., etal., 2013: Mental health outcomes at the Jersey shore after

Hurricane Sandy. Int. J. Emerg. Ment. Health, 15(3), 147–158.

Bose-O’Reilly, S., et al., 2021: COVID-19 and heat waves: New challenges

for healthcare systems. Environ. Res., 198, 111153, doi:10.1016/j.

envres.2021.111153.

Botana, L.M., 2016: Toxicological perspective on climate change: aquatic toxins.

Chem. Res. Toxicol., 29(4), 619–625, doi:10.1021/acs.chemrestox.6b00020.

Bouma, M., A. Siraj, X. Rodo and M. Pascual, 2016: El Nino-based malaria

epidemic warning for Oromia, Ethiopia, from August 2016 to July 2017. Trop.

Med. Int. Health, 21(11), 1481–1488, doi:10.1111/tmi.12776.

Bouman, T., L. Steg and T. Dietz, 2020: Insights from early COVID-19 responses

about promoting sustainable action. Nat. Sustain., doi:10.1038/s41893-020-

00626-x.

Bourke, S., etal., 2018: Evidence Review of Indigenous Culture for Health and

Wellbeing. Int. J. Health Wellness Soc., 8(4), 11-27

Bowen, K., D. Alexander, F. Miller and V. Dany, 2014a: Using social network

analysis to evaluate health-related adaptation decision-making in

Cambodia. Int. J. Environ. Res. Public Health, 11(2), 1605–1625, doi:10.3390/

ijerph110201605.

Bowen, K., K. Ebi and S. Friel, 2014b: Climate change adaptation and mitigation:

next steps for cross-sectoral action to protect global health. Mitig. Adapt.

Strateg. Glob. Change, 19(7), 1033–1040, doi:10.1007/s11027-013-9458-y.

Bowen, K., etal., 2021: Human health and climate change in the Paciﬁc: a

review of current knowledge. Glob. Transitions (In press).

1135

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Bowen, K.J. and K.L. Ebi, 2017: Health risks of climate change in the World

Health Organization South-East Asia Region. WHO South East Asia J. Public

Health, 6(2), 3–8.

Bowles, D.C., 2015: Climate change and health adaptation: consequences for

indigenous physical and mental health. Ann. Glob. Health, 81(3), 427–431,

doi:10.1016/j.aogh.2015.06.004.

Boyer, C., et al., 2020: Using implementation science for health adaptation:

opportunities for Paciﬁc island countries. Health Aff., 39(12), 2160–2167,

doi:10.1377/hlthaff.2020.01101.

Boyle, M., etal., 2020: Cities and the climate and ecological emergency: the

Liverpool city region response. J. Urban Regen. Renew., 13, 365–379.

Bradley, G.L., J.P. Reser, A.I. Glendon and M.C. Ellul, 2014: Distress and coping in

response to climate change. In: Stress and Anxiety: Applications to Social and

Environmental Threats, Psychological Well-being, Occupational Challenges,

and Developmental Psychology Climate Change, Lagos Verlag Berlin, Berlin.

pp. 33–42.

Bragg, C., et al., 2018: Remittances as aid following major sudden-onset

natural disasters. Disasters, 42(1), 3–18, doi:10.1111/disa.12229.

Brauer, M., J. Zhao, F. Bennitt and J. Stanaway, 2020: Global access to

handwashing: implications for COVID-19 control in low-income countries.

Environ. Health Perspect., 128(5), doi:10.1289/ehp7200.

Bremberg, N., H. Sonnsjö and M. Mobjörk, 2019: The EU and climate-related

security risks: a community of practice in the making? J. Eur. Integr., 41(5),

623–639.

Brenkert-Smith, H., R. Meldrum and A. Champ, 2015: Climate change beliefs

and hazard mitigation behaviors: homeowners and wildﬁre risk. Environ.

Hazards, 14(4), 341–360.

Broadbent, A.M., E.S. Krayenhoff and M. Georgescu, 2020: The motley drivers

of heat and cold exposure in 21st century US cities. Proc. Natl. Acad. Sci.

U.S.A., 117(35), 21108–21117, doi:10.1073/pnas.2005492117.

Bromet, E., etal., 2017: Post-traumatic stress disorder associated with natural

and human-made disasters in the World Mental Health Surveys. Psychol.

Med., 47(2), 227–241, doi:10.1017/s0033291716002026.

Brooks, H.E., 2013: Severe thunderstorms and climate change. Atmos. Res.,

123, 129–138, doi:10.1016/j.atmosres.2012.04.002.

Brosemer, K., etal., 2020: The energy crises revealed by COVID: intersections

of indigeneity, inequity, and health. Energy Res. Soc. Sci., 68, doi:10.1016/j.

erss.2020.101661.

Brown, M.R.G., et al., 2019: After the Fort McMurray wildﬁre there are

signiﬁcant increases in mental health symptoms in grade 7–12 students

compared to controls. BMC Psychiatry, 19, doi:10.1186/s12888-019-2074-y.

Brown, R.C., et al., 2017: Psychosocial interventions for children and

adolescents after man-made and natural disasters: a meta-analysis and

systematic review. Psychol. Med., 47(11), 1893–1905, doi:10.1017/

s0033291717000496.

Brubacher, J., etal., 2020: Associations of ﬁve food- and water-borne diseases

with ecological zone, land use and aquifer type in a changing climate. Sci.

Total Environ., 728, 138808, doi:10.1016/j.scitotenv.2020.138808.

Brügger, A., etal., 2015: Psychological responses to the proximity of climate

change. Nat. Clim. Change, 5(12), 1031–1037, doi:10.1038/nclimate2760.

Brunekreef, B., etal., 2021: Air pollution and COVID-19. Including Elements of Air

Pollution in Rural Areas, Indoor Air Pollution and Vulnerability and Resilience

Aspects of Our Society Against Respiratory Disease, Social Inequality

Stemming from Air Pollution Study for the Committee on Environment,

Public Health and Food Safety. Policy Department for Economic, Scientiﬁc

and Quality of Life Policies, European Parliament, Luxembourg.

Bryson, J., etal., 2021: Seasonality, climate change, and food security during

pregnancy among indigenous and non-indigenous women in rural Uganda:

implications for maternal-infant health. PLoS ONE, 16(3), doi:10.1371/

journal.pone.0247198.

Brzoska, M., 2018: Weather extremes, disasters, and collective violence:

conditions, mechanisms, and disaster-related policies in recent research.

Curr. Clim. Change Rep., 4(4), 320–329.

Buckley, R., etal., 2019: Economic value of protected areas via visitor mental

health. Nat. Commun., 10(1), 1–10, doi:10.1038/s41467-019-12631-6.

Buhaug, H., M. Croicu, H. Fjelde and N. v. Uexkull, 2020: A conditional model of

local income shock and civil conﬂict. The Journal of Politics, 83(1), 354-366.

Buhaug, H., etal., 2014: One effect to rule them all? A comment on climate and

conﬂict. Clim. Change, 127(3-4), 391–397.

Buhaug, H. and J. Vestby, 2019: On growth projections in the Shared

Socioeconomic Pathways. Glob. Environ. Polit., 19(4), 118–132.

Buhaug, H. and N. von Uexkull, 2021: Vicious circles: violence, vulnerability, and

climate change. Annu. Rev. Environ. Resour., 46, 545-568.

Bukvic, A. and G. Owen, 2017: Attitudes towards relocation following Hurricane

Sandy: should we stay or should we go? Disasters, 41(1), 101–123,

doi:10.1111/disa.12186.

Bunker, A., et al., 2016: Effects of air temperature on climate-sensitive

mortality and morbidity outcomes in the elderly; a systematic review and

meta-analysis of epidemiological evidence. EBioMedicine, 6, 258–268,

doi:10.1016/j.ebiom.2016.02.034.

Bunn, M., 2019: Nuclear disarmament, nuclear energy, and climate change:

exploring the linkages. Routledge, Oxfordshire.

Burkart, G., et al., 2021: Projecting global mortality due to non-optimal

temperature from 2020 to 2100: a global burden of disease forecasting

study. The Lancet., http://dx.doi.org/10.2139/ssrn.3723614.

Burke, M., etal., 2018: Higher temperatures increase suicide rates in the United

States and Mexico. Nat. Clim. Change, 8(8), 723, doi:10.1038/s41558-018-

0222-x.

Burke, M., S.M. Hsiang and E. Miguel, 2015a: Global non-linear effect of

temperature on economic production. Nature, 527(7577), 235–239,

doi:10.1038/nature15725.

Burke, M., S. M. Hsiang and E. Miguel, 2015b: Climate and conﬂict. Annu. Rev.

Econ., 7(1), 577–617, doi:10.1146/annurev-economics-080614-115430.

Burnham, M. and Z. Ma, 2017: Climate change adaptation: factors inﬂuencing

Chinese smallholder farmers’ perceived self-efﬁcacy and adaptation intent.

Reg. Environ. Change, 17(1), 171–186.

Busby, J., 2018: Taking stock: the ﬁeld of climate and security. Curr. Clim.

Change Rep., 4(4), 338–346.

Busby, J. and N. Krishnan, 2017: Widening the Scope to Asia: Climate Change

and Security. The Center for Climate and Security, Washington DC.

Busby, J., etal., 2018: In harm’s way: climate security vulnerability in Asia.

World Dev., 112, 88–118.

Butler, C.D. and D. Harley, 2010: Primary, secondary and tertiary effects of eco-

climatic change: the medical response. Postgrad. Med. J., 86(1014), 230,

doi:10.1136/pgmj.2009.082727.

Butler, T., etal., 2019: Aboriginal and Torres Strait Islander people’s domains of

wellbeing: a comprehensive literature review. Soc. Sci. Med., 233, 138–157,

doi:10.1016/j.socscimed.2019.06.004.

Butt, N., F. Lambrick, M. Menton and A. Renwick, 2019: The supply chain of

violence. Nat. Sustain., 2(8), 742–747.

Cai, R., S. Feng, M. Oppenheimer and M. Pytlikova, 2016: Climate variability

and international migration: the importance of the agricultural linkage. J.

Environ. Econ. Manag., 79, 135–151, doi:10.1016/j.jeem.2016.06.005.

Cai, W.J., etal., 2021: The 2020 China report of the Lancet Countdown on health

and climate change. Lancet Public Health, 6(1), E64–E81, doi:10.1016/

s2468-2667(20)30256-5.

Caini, S., F. Schellevis, C.E. Seblain and J. Paget, 2018: Important changes in the

timing of inﬂuenza epidemics in the WHO European Region over the past

20years: virological surveillance 1996 to 2016. Euro Surveill., 23(1), 2–10,

doi:10.2807/1560-7917.es.2018.23.1.17-00302.

Call, M., C. Gray, M. Yunus and M. Emch, 2017: Disruption, not displacement:

environmental variability and temporary migration in Bangladesh. Glob.

Environ. Change, 46, 157–165.

Camacho, A., etal., 2018: Cholera epidemic in Yemen, 2016–18: an analysis of

surveillance data. Lancet Glob. Health, 6(6), e680–e690, doi:10.1016/s2214-

109x(18)30230-4.

1136

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Caminade, C., K.M. McIntyre and A.E. Jones, 2019: Impact of recent and future

climate change on vector-borne diseases. Ann. N. Y. Acad. Sci., 1436(1),

157–173, doi:10.1111/nyas.13950.

Campagnolo, L. and M. Davide, 2019: Can the Paris deal boost SDGs

achievement? An assessment of climate mitigation co-beneﬁts or side-

effects on poverty and inequality. World Dev., 122, 96–109, doi:10.1016/j.

worlddev.2019.05.015.

Campbell, S., T.A. Remenyi, C.J. White and F.H. Johnston, 2018: Heatwave

and health impact research: a global review. Health Place, 53, 210–218,

doi:10.1016/j.healthplace.2018.08.017.

Canu, W.H., J.P. Jameson, E.H. Steele and M. Denslow, 2017: Mountaintop

removal coal mining and emergent cases of psychological disorder in

Kentucky. Community Ment. Health J., 53(7), 802–810, doi:10.1007/s10597-

017-0122-y.

Cárdenas, M.L. and E. Olivius, 2021: Building peace in the shadow of war:

women-to-women diplomacy as alternative peacebuilding practice in

Myanmar. J. Interv. Statebuild., 1–20. https://doi.org/10.1080/17502977.20

21.1917254

Carleton, T.A., 2017: Crop-damaging temperatures increase suicide rates

in India. Proc. Natl. Acad. Sci. U.S.A., 114(33), 8746–8751, doi:10.1073/

pnas.1701354114.

Carleton, T.A. and S.M. Hsiang, 2016: Social and economic impacts of climate.

Science, 353(6304).

Carlsten, C., S. Salvi, G.W.K. Wong and K.F. Chung, 2020: Personal strategies to

minimise effects of air pollution on respiratory health: advice for providers,

patients and the public. Eur. Respir. J., 55(6), doi:10.1183/13993003.02056-

2019.

Carlton, E., etal., 2016: A systematic review and meta-analysis of ambient

temperature and diarrhoeal diseases. Int. J. Epidemiol., 45(1), 117–130,

doi:10.1093/ije/dyv296.

Carman, J. and M. Zint, 2020: Deﬁning and classifying personal and household

climate change adaptation behaviors. Glob. Environ. Change Policy Dimens.,

61, doi:10.1016/j.gloenvcha.2020.102062.

Carrico, A.R. and K. Donato, 2019: Extreme weather and migration: evidence

from Bangladesh. Popul. Environ., 41(1), 1–31, doi:10.1007/s11111-019-

00322-9.

Carter, M. and S. Janzen, 2018: Social protection in the face of climate change:

targeting principles and ﬁnancing mechanisms. Environ. Dev. Econ., 23(3),

369–389, doi:10.1017/s1355770x17000407.

Caruso, R., I. Petrarca and R. Ricciuti, 2016: Climate change, rice crops,

and violence: evidence from Indonesia. J. Peace Res., 53(1), 66–83,

doi:10.1177/0022343315616061.

Carvajal, T.M., et al., 2018: Machine learning methods reveal the temporal

pattern of dengue incidence using meteorological factors in metropolitan

Manila, Philippines. BMC Infect. Dis., 18(1), 183, doi:10.1186/s12879-018-

3066-0.

Casado-Asensio, J., T. Kato and J. Sedemund, 2020: Climate resilient

development. In: Development Co-operation Report 2020: Learning from

Crises, Building Resilience [Oecd(ed.)]. OECD Publishing, Paris.

Cattaneo, C., etal., 2019: Human migration in the era of climate change. Rev.

Environ. Econ. Policy, 13(2), 189–206, doi:10.1093/reep/rez008.

Cattaneo, C. and G, Peri, 2016: The migration response to increasing

temperatures. J. Dev. Econ., 122, 127–146.

Cattaneo, C. and E. Massetti, 2019: Does harmful climate increase or decrease

migration? Evidence from rural households in Nigeria. Clim. Change Econ.,

10(4), doi:10.1142/s2010007819500131.

Cederman, L.-E. and N.B. Weidmann, 2017: Predicting armed conﬂict: time to

adjust our expectations? Science, 355(6324), 474–476.

Centers for Disease Control and Prevention., 2019: CDC’s Building Resilience

Against Climate Effects (BRACE) Framework. National Centre for

Environmental Health, Atlanta.

Céspedes-Báez, L.M.R. and F. J. Ruiz, 2018: ‘Peace without women does

not go!’ Women’s struggle for inclusion in Colombia’s peace process

with the FARC, Colombia Internacional, 83–109, https://doi.org/107440/

colombiaint94.2018.04.

Chadsuthi, S., S. Iamsirithaworn, W. Triampo and D.A. Cummings, 2016: The

impact of rainfall and temperature on the spatial progression of cases during

the chikungunya re-emergence in Thailand in 2008–2009. Trans. R. Soc. Trop.

Med. Hyg., 110(2), 125–133, doi:10.1093/trstmh/trv114.

Chambers, J., 2020: Global and cross-country analysis of exposure of vulnerable

populations to heatwaves from 1980 to 2018. Clim. Change, 163(1), 539–

558, doi:10.1007/s10584-020-02884-2.

Chan, E., etal., 2018: Association between ambient temperatures and mental

disorder hospitalizations in a subtropical city: a time-series study of Hong

Kong special administrative region. Int. J. Environ. Res. Public Health, 15(4),

754.

Chan, E.Y.Y. and S. Peijun, 2017: Health and risks: integrating health into disaster

risk reduction, risk communication, and building resilient communities. Int. J.

Disaster Risk Sci., 8(2), 107–108, doi:10.1007/s13753-017-0131-z.

Chandra, A., et al., 2017: Gendered vulnerabilities of smallholder farmers

to climate change in conﬂict-prone areas: a case study from Mindanao,

Philippines. J. Rural Stud., 50, 45–59, doi:10.1016/j.jrurstud.2016.12.011.

Chang, A.Y., etal., 2019: Past, present, and future of global health ﬁnancing:

a review of development assistance, government, out-of-pocket, and

other private spending on health for 195 countries, 1995–2050. Lancet,

393(10187), 2233–2260.

Chang, C.C. and S.C. Chen, 2015: Colliding epidemics and the rise of

cryptococcosis. J. Fungi, 2(1), doi:10.3390/jof2010001.

Chang, C.J., C.S. Chen, C.J. Tien and M.R. Lu, 2018: Epidemiological, clinical

and climatic characteristics of dengue fever in Kaohsiung City, Taiwan

with implication for prevention and control. PLoS ONE, 13(1), e190637,

doi:10.1371/journal.pone.0190637.

Chang, K.M., et al., 2017: Ancillary health effects of climate mitigation

scenarios as drivers of policy uptake: a review of air quality, transportation

and diet co-beneﬁts modeling studies. Environ. Res. Lett., 12(11), 113001,

doi:10.1088/1748-9326/aa8f7b.

Chapin, III, F.S., etal., 2016: Community-empowered adaptation for self-reliance.

Curr. Opin. Environ. Sustain., 19, 67–75, doi:10.1016/j.cosust.2015.12.008.

Chapra, S.C., etal., 2017: Climate change impacts on harmful algal blooms in

US freshwaters: a screening-level assessment. Environ. Sci. Technol., 51(16),

8933–8943.

Charlesworth, K. and M. Jamieson, 2019: Healthcare in a carbon-constrained

world. Aust. Health Rev., 43(3), 241–245, doi:10.1071/ah17184.

Charlesworth, K.E. and M. Jamieson, 2018: Healthcare in a carbon-constrained

world. Aust. Health Rev., doi:10.1071/ah17184.

Charlesworth, K.E., G.J. Stewart and P. Sainsbury, 2018: Addressing the carbon

footprint of health organisations: eight lessons for implementation. Public

Health Res. Pract., 28(4), doi:10.17061/phrp2841830.

Charlson, F., etal., 2021: Climate change and mental health: a scoping review.

Int. J. Environ. Res. Public Health, 18(9), doi:10.3390/ijerph18094486.

Chen, C.C., etal., 2021a: Projection of future temperature extremes, related

mortality, and adaptation due to climate and population changes in Taiwan.

Sci. Total Environ., 760, doi:10.1016/j.scitotenv.2020.143373.

Chen, J., etal., 2020: Global socioeconomic exposure of heat extremes under

climate change. J. Clean. Prod., 277, doi:10.1016/j.jclepro.2020.123275.

Chen, J.J., V. Mueller, Y. Jia and S. Tseng, 2017: Validating migration responses

to ﬂooding using satellite and vital registration data. Am. Econ. Rev., 107(5),

441–445.

Chen, K., et al., 2019: Temporal variations in the triggering of myocardial

infarction by air temperature in Augsburg, Germany, 1987–2014. Eur. Heart.

J., doi:10.1093/eurheartj/ehz116.

Chen, K., et al., 2018: Future ozone-related acute excess mortality under

climate and population change scenarios in China: a modeling study. PLoS

Med., 15(7), doi:10.1371/journal.pmed.1002598.

1137

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Chen, L., et al., 2021b: The occurrence and control of waterborne viruses

in drinking water treatment: a review. Chemosphere, 281, 130728,

doi:10.1016/j.chemosphere.2021.130728.

Chen, M., 2015: Self-efﬁcacy or collective efﬁcacy within the cognitive theory

of stress model: Which more effectively explains people’s self-reported

proenvironmental behavior? J. Environ. Psychol., 42, 66–75, doi:10.1016/j.

jenvp.2015.02.002.

Cheng, A., et al., 2017: Analyzing the potential risk of climate change on

Lyme disease in Eastern Ontario, Canada using time series remotely sensed

temperature data and tick population modelling. Remote Sens., 9(6),

doi:10.3390/rs9060609.

Cheng, J. and P. Berry, 2013: Development of key indicators to quantify the

health impacts of climate change on Canadians. Int. J. Public Health, 58(5),

765–775, doi:10.1007/s00038-013-0499-5.

Cheng, J., etal., 2018: Heatwave and elderly mortality: an evaluation of death

burden and health costs considering short-term mortality displacement.

Environ. Int., 115, 334–342, doi:10.1016/j.envint.2018.03.041.

Cheng, J., etal., 2019: Impacts of heat, cold, and temperature variability on

mortality in Australia, 2000–2009. Sci. Total Environ., 651, 2558–2565,

doi:10.1016/j.scitotenv.2018.10.186.

Cheng, J., etal., 2016: Impacts of temperature change on ambulance dispatches

and seasonal effect modiﬁcation. Int. J. Biometeorol., 60(12), 1863–1871,

doi:10.1007/s00484-016-1173-4.

Cheng, L., etal., 2020: Record-setting ocean warmth continued in 2019. Adv.

Atmos. Sci., 37(2), 137–142, doi:10.1007/s00376-020-9283-7.

Cheng, S., etal., 2021: The effect of average temperature on suicide rates in

ﬁve urban California counties 1999–2019: an ecological time series analysis.

BMC public health, 21(1), 1-9.

Chersich, M.F., F. Scorgie, H. Rees and C.Y. Wright, 2018: How climate change

can fuel listeriosis outbreaks in South Africa. S. Afr. Med. J., 108(6), 453–454,

doi:10.7196/SAMJ.2018.v108i6.13274.

Chhetri, B.K., etal., 2019: Projected local rain events due to climate change and

the impacts on waterborne diseases in Vancouver, British Columbia, Canada.

Environ. Health, 18(1), 116, doi:10.1186/s12940-019-0550-y.

Chiabai, A., et al., 2018a: The nexus between climate change, ecosystem

services and human health: towards a conceptual framework. Sci. Total

Environ., 635, 1191–1204, doi:10.1016/j.scitotenv.2018.03.323.

Chiabai, A., J.V. Spadaro and M.B. Neumann, 2018b: Valuing deaths or

years of life lost? Economic beneﬁts of avoided mortality from early heat

warning systems. Mitig. Adapt. Strateg. Glob. Change, 23(7), 1159–1176,

doi:10.1007/s11027-017-9778-4.

Chindarkar, N., 2012: Gender and climate change-induced migration:

proposing a framework for analysis. Environ. Res. Lett., 7(2), 25601–25601,

doi:10.1088/1748-9326/7/2/025601.

Choi, C., P. Berry and A. Smith, 2021: The climate beneﬁts, co-beneﬁts, and

trade-offs of green infrastructure: a systematic literature review. J. Environ.

Manag., 291, doi:10.1016/j.jenvman.2021.112583.

Chong, K.C., etal., 2020a: Association between meteorological variations and

activities of inﬂuenza A and B across different climate zones: a multi-region

modelling analysis across the globe. J. Infect., 80(1), 84–98, doi:10.1016/j.

jinf.2019.09.013.

Chong, K.C., etal., 2020b: Latitudes mediate the association between inﬂuenza

activity and meteorological factors: a nationwide modelling analysis

in 45 Japanese prefectures from 2000 to 2018. Sci. Total Environ., 703,

doi:10.1016/j.scitotenv.2019.134727.

Chort, I. and M. de la Rupelle, 2016: Determinants of Mexico-U.S. outward

and return migration ﬂows: a state-level panel data analysis. Demography,

53(5), 1453–1476.

Choularton, R. and P. Krishnamurthy, 2019: How accurate is food security early

warning? Evaluation of FEWS NET accuracy in Ethiopia. Food Sec., 11(2),

333–344, doi:10.1007/s12571-019-00909-y.

Chowdhury, F.R., etal., 2018a: The association between temperature, rainfall

and humidity with common climate-sensitive infectious diseases in

Bangladesh. PLoS ONE, 13(6), e199579, doi:10.1371/journal.pone.0199579.

Chowdhury, S., S. Dey and K.R. Smith, 2018b: Ambient PM2.5 exposure and

expected premature mortality to 2100 in India under climate change

scenarios. Nat. Commun., 9(1), 318, doi:10.1038/s41467-017-02755-y.

Chu, E. and K. Michael, 2018: Recognition in urban climate justice: marginality

and exclusion of migrants in Indian cities. Environ. Urban., 31(1), 139–156,

doi:10.1177/0956247818814449.

Chuang, T.W., L.F. Chaves and P.J. Chen, 2017: Effects of local and regional

climatic ﬂuctuations on dengue outbreaks in southern Taiwan. PLoS ONE,

12(6), e178698, doi:10.1371/journal.pone.0178698.

Chun, H.B.C., K. Hong, H. Hwang and S. Sohn, 2019: Effects of the El Niño

southern oscillation on inﬂuenza peak activity timing. Online J. Public Health

Inform., 11(1).DOI: https://doi.org/10.5210/ojphi.v11i1.9834

Chung, Y., etal., 2018: Changing susceptibility to non-optimum temperatures

in Japan, 1972–2012: the role of climate, demographic, and socioeconomic

factors. Environ. Health Perspect., 126(5), 57002–57002, doi:10.1289/

ehp2546.

Cianconi, P., S. Betro and L. Janiri, 2020: The impact of climate change on mental

health: a systematic descriptive review. Front. Psychiatry, 11, doi:10.3389/

fpsyt.2020.00074.

Cidro, J., B. Adekunle, E. Peters and T. Martens, 2015: Beyond food security:

understanding access to cultural food for urban indigenous people in

Winnipeg as indigenous food sovereignty. Can. J. Urban Res., 24, 24.

Cil, G. and T.A. Cameron, 2017: Potential climate change health risks from

increases in heat waves: abnormal birth outcomes and adverse maternal

health conditions. Risk Anal., 37(11), 2066–2079, doi:10.1111/risa.12767.

Cisse, G., 2019: Food-borne and water-borne diseases under climate

change in low- and middle-income countries: further efforts needed for

reducing environmental health exposure risks. Acta Trop., 194, 181–188,

doi:10.1016/j.actatropica.2019.03.012.

Cissé, G., J.A. Menezes and U. Confalonieri, 2018: Climate-sensitive infectious

diseases. In: The Adaptation Gap Report 2018 [Unep(ed.)]. United Nations

Environment Programme (UNEP), Nairobi, Kenya, pp. 49–59. ISBN 978-

9280737288.

Cissé, G., etal., 2016: Vulnerabilities of water and sanitation at households

and community levels in face of climate variability and change: trends from

historical climate time series in a West African medium-sized town. Int. J.

Glob. Environ. Issues, 15(1/2), 81–99.

Claeys, M.J., S. Rajagopalan, T.S. Nawrot and R.D. Brook, 2017: Climate and

environmental triggers of acute myocardial infarction. Eur. Heart J., 38(13),

955–960, doi:10.1093/eurheartj/ehw151.

Clare, A., R. Graber, L. Jones and D. Conway, 2017: Subjective measures of

climate resilience: What is the added value for policy and programming?

Glob. Environ. Change Policy Dimens., 46, 17–22, doi:10.1016/j.

gloenvcha.2017.07.001.

Clark, M. and D. Tilman, 2017: Comparative analysis of environmental impacts

of agricultural production systems, agricultural input efﬁciency, and food

choice. Environ. Res. Lett., 12(6), doi:10.1088/1748-9326/aa6cd5.

Clayton, S. and B. Karazsia, 2020: Development and validation of a measure

of climate change anxiety. J. Environ. Psychol., 69, doi:10.1016/j.

jenvp.2020.101434.

Clayton, S., C.M. Manning, K. Krygsman and M. Speiser, 2017: Mental Health

and Our Changing Climate: Impacts, Implications, and Guidance. American

Psychological Association, and ecoAmerica, Washington, DC. 69 pp.

Clow, K.M., et al., 2017: Northward range expansion of Ixodes scapularis

evident over a short timescale in Ontario, Canada. Plos One, 12(12),

doi:10.1371/journal.pone.0189393.

Cohen, B., E. Tyler and T. Gunfas, 2017: Lessons from co-impacts assessment

under the Mitigation Action Plans and Scenarios (MAPS) Programme. Clim.

Policy, 17(8), 1065–1075, doi:10.1080/14693062.2016.1222258.

1138

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Collaco, J.M., L.J. Appel, J. McGready and G.R. Cutting, 2018: The relationship

of lung function with ambient temperature. PLoS ONE, 13(1), e191409,

doi:10.1371/journal.pone.0191409.

Colon-Gonzalez, F., C. Fezzi, I. Lake and P. Hunter, 2013: The effects of weather

and climate change on dengue. PLoS Negl. Trop. Dis., 7(11), doi:10.1371/

journal.pntd.0002503.

Confalonieri, U., J.A. Menezes and C. Margonari, 2017: Environmental change

and the emergence of infectious diseases: a regional perspective from South

America. In: Examining the Role of Environmental Change on Emerging

Infectious Diseases and Pandemics, pp. 109–137. IGI Global, Hershey PA.

Confalonieri, U. E., J.A. Menezes and C. Margonari de Souza, 2015: Climate

change and adaptation of the health sector: the case of infectious diseases.

Virulence, 6(6), 554–557, doi:10.1080/21505594.2015.1023985.

Coniglio, N.D. and G. Pesce, 2015: Climate variability and international

migration: an empirical analysis. Envir. Dev. Econ., 20(4), 438–468.

Connell, J., 2016: Last days in the Carteret Islands? Climate change, livelihoods

and migration on coral atolls. Asia Pac. Viewp., 57(1), 3–15.

Connolly, M., 2013: Some like it mild and not too wet: the inﬂuence of weather

on subjective well-being. J. Happiness Stud., 14(2), 457–473, doi:10.1007/

s10902-012-9338-2.

Corvalan, C., et al., 2020: Towards climate resilient and environmentally

sustainable health care facilities. Int. J. Environ. Res. Public Health, 17(23),

doi:10.3390/ijerph17238849.

Costa, A. F., G. Hoek, B. Brunekreef and A. de Leon, 2017: Air pollution and

deaths among elderly residents of Sao Paulo, Brazil: an analysis of mortality

displacement. Environ. Health Perspect., 125(3), 349–354, doi:10.1289/

ehp98.

Coughlan de Perez, E., et al., 2021: Epidemiological versus meteorological

forecasts: best practice for linking models to policy making. Int. J. Forecast.

DOI: https://doi.org/10.1016/j.ijforecast.2021.08.003

Crawford, A. and C. Church, 2020: The NAP Process and Peacebuilding. NAP

Global Network Brieﬁng Note. International Institute for Sustainable

Development (IISD), Geneva.

Crawford, A., etal., 2015: Promoting Climate-resilient Peacebuilding in Fragile

States. International Institute for Sustainable Development (IISD), Geneva.

Cromar, K.R., M. Ghazipura, L.A. Gladson and L. Perlmutt, 2020: Evaluating the

US Air Quality Index as a risk communication tool: comparing associations

of index values with respiratory morbidity among adults in California. PLoS

ONE, 15(11), doi:10.1371/journal.pone.0242031.

Cunado, J. and F.P. de Gracia, 2013: Environment and happiness: new evidence

for Spain. Soc. Indic. Res., 112(3), 549–567, doi:10.1007/s11205-012-0038-

Cundill, G., et al., 2021: Toward a climate mobilities research agenda:

intersectionality, immobility, and policy responses. Glob. Environ. Change,

69. DOI: https://doi.org/10.1016/j.gloenvcha.2021.102315 Available at:

https://www.sciencedirect.com/science/article/pii/S0959378021000947.

Cunsolo, A., etal., 2020: “You can never replace the caribou”: Inuit experiences

of ecological grief from caribou declines. Am. Imago, 77(1), 31–59,

doi:10.1353/aim.2020.0002.

Cunsolo, A. and N.R. Ellis, 2018: Ecological grief as a mental health response to

climate change-related loss. Nat. Clim. Change, 8(4), 275–281, doi:10.1038/

s41558-018-0092-2.

Cunsolo Willox, A., et al., 2013: Climate change and mental health: an

exploratory case study from Rigolet, Nunatsiavut, Canada. Clim. Change,

121(2), 255–270.

Cunsolo Willox, A., et al., 2015: Examining relationships between climate

change and mental health in the Circumpolar North. Reg. Environ. Change,

15(1), 169–182.

Curriero, F.C., J.A. Patz, J.B. Rose and S. Lele, 2001: The association between

extreme precipitation and waterborne disease outbreaks in the United

States, 1948–1994. Am. J. Public Health, 91(8), 1194–1199, doi:10.2105/

ajph.91.8.1194.

Curtis, K., E. Fussell and J. DeWaard, 2015: Recovery migration after Hurricanes

Katrina and Rita: spatial concentration and intensiﬁcation in the migration

system. Demography, 52(4), 1269–1293.

Curtis, S., etal., 2017: Impact of extreme weather events and climate change

for health and social care systems. Environ. Health, 16, 23–32, doi:10.1186/

s12940-017-0324-3.

Cusack, L., C. de Crespigny and P. Athanasos, 2011: Heatwaves and their impact

on people with alcohol, drug and mental health conditions: a discussion

paper on clinical practice considerations. J. Adv. Nurs., 67(4), 915–922,

doi:10.1111/j.1365-2648.2010.05551.x.

D’Amato, G., etal., 2020: The effects of climate change on respiratory allergy

and asthma induced by pollen and mold allergens. Allergy, 75(9), 2219–

2228, doi:10.1111/all.14476.

D’Odorico, P., etal., 2018: The global food-energy-water nexus. Rev. Geophys.,

56(3), 456–531.

Dahl, K., R. Licker, J.T. Abatzoglou and J. Declet-Barreto, 2019: Increased

frequency of and population exposure to extreme heat index days in

the United States during the 21st century. Environ. Res. Commun., 1(7),

doi:10.1088/2515-7620/ab27cf.

Damschroder, L.J., et al., 2009: Fostering implementation of health services

research ﬁndings into practice: a consolidated framework for advancing

implementation science. Implement. Sci., 4(1), 50.

Daniel, M., et al., 2018: Increased relative risk of tick-borne encephalitis

in warmer weather. Front. Cell. Infect. Microbiol., 8, doi:10.3389/

fcimb.2018.00090.

Dannenberg, A., H. Frumkin, J. Hess and K. Ebi, 2019: Managed retreat as a

strategy for climate change adaptation in small communities: public health

implications. Clim. Change, 153(1-2), 1–14, doi:10.1007/s10584-019-

02382-0.

Dar, O., etal., 2014: Integrating health into disaster risk reduction strategies:

key considerations for success. Am. J. Public Health, 104(10), 1811–1816,

doi:10.2105/ajph.2014.302134.

Dasandi, N., H. Graham, P. Lampard and S. Mikhaylov, 2021: Engagement with

health in national climate change commitments under the Paris Agreement:

a global mixed-methods analysis of the nationally determined contributions.

Lancet Planet. Health, 5(2), 93–101.

David-Chavez, D. and M. Gavin, 2018: A global assessment of Indigenous

community engagement in climate research. Environ. Res. Lett., 13(12),

doi:10.1088/1748-9326/aaf300.

Davies, M., etal., 2013: Promoting resilient livelihoods through adaptive social

protection: lessons from 124 programmes in South Asia. Dev. Policy Rev., 31,

doi:10.1111/j.1467-7679.2013.00600.x.

Davis, R.E., etal., 2016: Cold, dry air is associated with inﬂuenza and pneumonia

mortality in Auckland, New Zealand. Inﬂuenza Other Respi. Viruses, 10(4),

310–313, doi:10.1111/irv.12369.

Day, A. and J. Caus, 2020: Conﬂict Prevention in the Era of Climate Change:

Adapting the UN to Climate-Security Risks. United Nations University, New

York, ISBN 978-9280865141.

De’Donato, F., etal., 2018: Temporal variation in the effect of heat and the

role of the Italian heat prevention plan. Public Health, 161, 154–162,

doi:10.1016/j.puhe.2018.03.030.

De Châtel, F., 2014: The role of drought and climate change in the Syrian

uprising: untangling the triggers of the revolution. Middle East. Stud., 50(4),

521–535.

De Dominicis, S., etal., 2015: We are at risk, and so what? Place attachment,

environmental risk perceptions and preventive coping behaviours. J. Environ.

Psychol., 43, 66–78.

De Juan, A., 2015: Long-term environmental change and geographical patterns

of violence in Darfur, 2003–2005. Polit. Geogr., 45, 22–33.

de la Vega-Leinert, A.C., S. Stoll-Kleemann and E. Wegener, 2018: Managed

realignment (MR) along the Eastern German Baltic Sea: a catalyst for conﬂict

or for a coastal zone management consensus. J. Coast. Res., 34(3), 586–601,

doi:10.2112/jcoastres-d-15-00217.1.

1139

Health, Wellbeing and the Changing Structure of Communities Chapter 7

de Leon-Martinez, L., et al., 2020: Critical review of social, environmental

and health risk factors in the Mexican indigenous population and their

capacity to respond to the COVID-19. Sci. Total Environ., 733, doi:10.1016/j.

scitotenv.2020.139357.

de Lima, C., etal., 2021: Heat stress on agricultural workers exacerbates crop

impacts of climate change. Environ. Res. Lett., 16(4), doi:10.1088/1748-

9326/abeb9 f.

De Neve, J.-E., E. Diener, L. Tay and C. Xuereb, 2013: The objective beneﬁts of

subjective well-being. In: World Happiness Report 2013 [Helliwell, J.L.R. and

J. e. Sachs(eds.)]. UN Sustainable Development Solutions Network, New York

(Available at: https://ssrn.com/abstract=2306651).

Defor, A. and T.V. Oheneba-Dornyo, 2020: Is climate change hampering

global health security? A review of the evidence. School of Diplomacy and

International Relations Seton Hall University, New Jersey.

Degen, A. and S. El-Meccawi, 2009: Livestock production among urban Negev

Bedouin. Outlook Agric., 38(4), 327–335, doi:10.5367/000000009790422106.

Delhey, J. and G. Dragolov, 2016: Happier together. Social cohesion and

subjective well-being in Europe. Int. J. Psychol., 51(3), 163–176, doi:10.1002/

ijop.12149.

Dellink, R., J. Chateau, E. Lanzi and B. Magné, 2017: Long-term economic

growth projections in the Shared Socioeconomic Pathways. Glob. Environ.

Change, 42, 200–214.

Demain, J., 2018: Climate change and the impact on respiratory and allergic

disease: 2018. Curr. Allergy Asthma Rep., 18(4), 1–5, doi:10.1007/s11882-

018-0777-7.

Deng, S.Z., etal., 2020: Climate change, air pollution, and allergic respiratory

diseases: a call to action for health professionals. Chin. Med. J., 133(13),

1552–1560, doi:10.1097/cm9.0000000000000861.

Dennekamp, M., etal., 2015: Forest ﬁre smoke exposures and out-of-hospital

cardiac arrests in Melbourne, Australia: a case-crossover study. Environ.

Health Perspect., 123(10), 959–964, doi:10.1289/ehp.1408436.

Desai, B., et al., 2021: Addressing the human cost in a changing climate.

Science, 372(6548), 1284–1287. Available at: http://science.sciencemag.

org/content/372/6548/1284.abstract.

Deshpande, A., H.H. Chang and K. Levy, 2020: Heavy rainfall events and

diarrheal diseases: the role of urban-rural geography. Am. J. Trop. Med. Hyg.,

103(3), 1043–1049, doi:10.4269/ajtmh.19-0768.

Deslatte, A., 2020: The erosion of trust during a global pandemic and how

public administrators should counter it. Am. Rev. Public Adm., 50(6-7), 489–

496, doi:10.1177/0275074020941676.

DeWaard, J., K.J. Curtis and E. Fussell, 2016: Population recovery in New Orleans

after Hurricane Katrina: exploring the potential role of stage migration in

migration systems. Popul. Environ., 37(4), 449–463, doi:10.1007/s11111-

015-0250-7.

Deyle, E.R., etal., 2016: Global environmental drivers of inﬂuenza. Proc. Natl.

Acad. Sci. U.S.A., 113(46), 13081–13086, doi:10.1073/pnas.1607747113.

Dialesandro, J., N. Brazil, S. Wheeler and Y. Abunnasr, 2021: Dimensions of

thermal inequity: neighborhood social demographics and urban heat in

the southwestern US. Int. J. Environ. Res. Public Health, 18(3), doi:10.3390/

ijerph18030941.

Diaz-Mendez, S.E., etal., 2018: Economic, environmental and health co-beneﬁts

of the use of advanced control strategies for lighting in buildings of Mexico.

Energy Policy, 113, 401–409, doi:10.1016/j.enpol.2017.11.028.

Diaz, D.B., 2016: Estimating global damages from sea level rise with the Coastal

Impact and Adaptation Model (CIAM). Clim. Change, 137(1), 143–156.

Diaz, J., etal., 2018: Time trend in the impact of heat waves on daily mortality

in Spain for a period of over thirty years (1983–2013). Environ. Int., 116,

10–17, doi:10.1016/j.envint.2018.04.001.

Diaz, J., etal., 2019: Time trends in the impact attributable to cold days in Spain:

incidence of local factors. Sci. Total Environ., 655, 305–312, doi:10.1016/j.

scitotenv.2018.11.254.

Diaz, J., et al., 2015: Comparison of the effects of extreme temperatures

on daily mortality in Madrid (Spain), by age group: the need for a cold

wave prevention plan. Environ. Res., 143(Pt A), 186–191, doi:10.1016/j.

envres.2015.10.018.

Dicker, D., et al., , 2018a: Global, regional, and national age-sex-speciﬁc

mortality and life expectancy, 1950–2017: a systematic analysis for the

Global Burden of Disease Study 2017. The Lancet, 392(10159), 1684–1735,

doi:10.1016/S0140-6736(18)31891-9.

Diener, E. and L. Tay, 2015: Subjective well-being and human welfare around

the world as reﬂected in the Gallup World Poll. Int. J. Psychol., 50(2), 135–

149, doi:10.1002/ijop.12136.

Dillender, M., 2021: Climate change and occupational health are there limits

to our ability to adapt? J. Hum. Resour., 56(1), 184–224, doi:10.3368/

jhr.56.1.0718-9594R3.

Dilling, L., etal., 2017: Drivers of adaptation: responses to weather- and climate-

related hazards in 60 local governments in the Intermountain Western U.S.

Environ. Plan.A, 49(11), 2628–2648, doi:10.1177/0308518x16688686.

Dimitrova, A., etal., 2021: Association between ambient temperature and heat

waves with mortality in South Asia: systematic review and meta-analysis.

Environ. Int., 146, doi:10.1016/j.envint.2020.106170.

Dinar, S., D. Katz, L. De Stefano and B. Blankespoor, 2015: Climate change,

conﬂict, and cooperation: global analysis of the effectiveness of international

river treaties in addressing water variability. Polit. Geogr., 45, 55–66.

Ding, H.L., H.J. Li and N.N. Sze, 2021: Effects of the abolishment of London

western charging zone on trafﬁc ﬂow and vehicle emissions. Int. J. Sustain.

Transp., doi:10.1080/15568318.2021.1914790.

Djennad, A., et al., 2019: Seasonality and the effects of weather on

Campylobacter infections. BMC Infect. Dis., 19(1), 255, doi:10.1186/s12879-

019-3840-7.

Djoudi, H., etal., 2016: Beyond dichotomies: gender and intersecting inequalities

in climate change studies. Ambio, 45(3), S248–S262.

Doan, V.Q. and H. Kusaka, 2018: Projections of urban climate in the 2050s in a

fast-growing city in Southeast Asia: the greater Ho Chi Minh City metropolitan

area, Vietnam. Int. J. Climatol., 38(11), 4155–4171, doi:10.1002/joc.5559.

Dockery, A., 2020: Inter-generational transmission of indigenous culture and

children’s wellbeing: evidence from Australia. Int. J. Intercult. Relat., 74,

80–93, doi:10.1016/j.ijintrel.2019.11.001.

Dodd, W., etal., 2018: The summer of smoke: ecosocial and health impacts of

a record wildﬁre season in the Northwest Territories, Canada. Lancet Glob.

Health, 6, S30–S30, doi:10.1016/s2214-109x(18)30159-1.

Dodd, W., etal., 2017: Determinants of internal migrant health and the healthy

migrant effect in South India: a mixed methods study. BMC Int. Health Hum.

Rights, 17, doi:10.1186/s12914-017-0132-4.

Dodds, K., et al., 2020: The COVID-19 pandemic: territorial, political and

governance dimensions of the crisis. Territ. Polit. Gov., 8(3), 289–298, doi:10

.1080/21622671.2020.1771022.

Doherty, R.M., M.R. Heal and F.M. O’Connor, 2017: Climate change impacts on

human health over Europe through its effect on air quality. Environ. Health,

16, 33–44, doi:10.1186/s12940-017-0325-2.

Dombrowsky, I., 2010: The role of intra-water sector issue linkage in the

resolution of transboundary water conﬂicts. Water Int., 35(2), 132–149.

Domínguez-Morueco, N., etal., 2019: Combining monitoring and modelling

approaches for BaP characterization over a petrochemical area. Sci. Total

Environ., 658, 424–438, doi:10.1016/j.scitotenv.2018.12.202.

Donatuto, J., L. Campbell and W. Trousdale, 2020: The “value” of values-driven

data in identifying Indigenous health and climate change priorities. Clim.

Change, 158(2), 161–180, doi:10.1007/s10584-019-02596-2.

Dong, C., etal., 2019: Wastewater treatment in amine-based carbon capture.

Chemosphere, 222, 742–756, doi:10.1016/j.chemosphere.2019.01.038.

Dosio, A., L. Mentaschi, E. Fischer and K. Wyser, 2018: Extreme heat waves under

1.5°C and 2°C global warming. Environ. Res. Lett., 13, doi:10.1088/1748-

9326/aab827.

DPPA, UNDP and UNEP, 2020: United Nations Climate Security Mechanism

Toolbox Brieﬁng Note. Climate Security Mechanism, New York.

1140

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Dresse, A., I. Fischhendler, J.Ø. Nielsen and D. Zikos, 2019: Environmental

peacebuilding: towards a theoretical framework. Coop. Conﬂ., 54(1), 99–119.

Drewnowski, A., et al., 2020: Toward healthy diets from sustainable food

systems. Curr. Dev. Nutr., 4(6), doi:10.1093/cdn/nzaa083.

Duane, B., et al., 2019: Environmentally sustainable dentistry: a brief

introduction to sustainable concepts within the dental practice. Br. Dent. J.,

226, 292–295, doi:10.1038/s41415-019-0010-7.

Duarte, J.L., F.A. Diaz-Quijano, A.C. Batista and L.L. Giatti, 2019: Climatic

variables associated with dengue incidence in a city of the Western Brazilian

Amazon region. Rev. Soc. Bras. Med. Trop., 52, e20180429, doi:10.1590/0037-

8682-0429-2018.

Dube, E. and E. Munsaka, 2018: The contribution of indigenous knowledge to

disaster risk reduction activities in Zimbabwe: a big call to practitioners.

Jamba J. Disaster Risk Stud., 10, doi:10.4102/jamba.v10i1.493.

Dudgeon, P., A. Bray, B. D’Costa and R. Walker, 2017: Decolonising psychology:

validating social and emotional wellbeing. Aust. Psychol., 52(4), 316–325,

doi:10.1111/ap.12294.

Dudley, J.P., E.P. Hoberg, E.J. Jenkins and A.J. Parkinson, 2015: Climate change

in the North American Arctic: a one health perspective. EcoHealth, 12(4),

713–725, doi:10.1007/s10393-015-1036-1.

Dumic, I. and E. Severnini, 2018: “Ticking bomb”: the impact of climate

change on the incidence of Lyme disease. Can. J. Infect. Dis. Med. Microbiol.,

5719081, doi:10.1155/2018/5719081.

Dunn, H. and R. Matthew, 2015: Natural resources and gender in conﬂict

settings. Peace Rev. J. Soc. Justice, 27(2), 156–164, doi:10.1080/1040265

9.2015.1037619.

Durkalec, A., C. Furgal, M. Skinner and T. Sheldon, 2015: Climate change

inﬂuences on environment as a determinant of indigenous health:

relationships to place, sea ice, and health in an Inuit community. Soc. Sci.

Med., 136, 17–26.

Ebi, K., 2011: Climate change and health risks: assessing and responding

to them through ‘adaptive management’. Health Aff., 30(5), 924–930,

doi:10.1377/hlthaff.2011.0071.

Ebi, K., 2016: Adaptation and resilience. Public Health Rev., 37, doi:10.1186/

s40985-016-0032-5.

Ebi, K., etal., 2020: Using detection and attribution to quantify how climate

change is affecting health. Health Aff., 39(12), 2168–2174, doi:10.1377/

hlthaff.2020.01004.

Ebi, K., etal., 2018a: Stress testing the capacity of health systems to manage

climate change-related shocks and stresses. Int. J. Environ. Res. Public

Health, 15(11), doi:10.3390/ijerph15112370.

Ebi, K., etal., 2018b: Health risks of warming of 1.5degrees C, 2degrees C,

and higher, above pre-industrial temperatures. Environ. Res. Lett., 13(6),

doi:10.1088/1748-9326/aac4bd.

Ebi, K., etal., 2021a: Burning embers: synthesis of the health risks of climate

change. Environ. Res. Lett., 16(4), doi:10.1088/1748-9326/abeadd.

Ebi, K., etal., 2021b: Interactions between two existential threats: COVID-19

and climate change, Clim Risk Manag, doi: 10.1016/j.crm.2021.100363

Ebi, K. and J. Hess, 2020: Health risks due to climate change: inequity in

causes and consequences. Health Aff., 39(12), 2056–2062, doi:10.1377/

hlthaff.2020.01125.

Ebi, K. and I. Loladze, 2019: Elevated atmospheric CO2 concentrations and

climate change will affect our food’s quality and quantity. Lancet Planet.

Health, 3(7), E283–E284, doi:10.1016/s2542-5196(19)30108-1. Comment.

Ebi, K., 2019: Effective heat action plans: research to interventions. Environ.

Res. Lett., 14(12), doi:10.1088/1748-9326/ab5ab0.

Ebi, K., N.H. Ogden, J.C. Semenza and A. Woodward, 2017: Detecting and

attributing health burdens to climate change. Environ. Health Perspect.,

125(8), 85004, doi:10.1289/EHP1509.

Ebi, K. and M. Otmani del Barrio, 2017: Lessons learned on health adaptation to

climate variability and change: experiences across low- and middle-income

countries. Environ. Health Perspect., 125(6), doi:10.1289/EHP405.

Ebrahim, S., etal., 2020: Forward planning for disaster-related mass gatherings

amid COVID-19. Lancet Planet. Health, 4(9), E379–E380, doi:10.1016/

s2542-5196(20)30175-3.

Echenique, M. and L. Melgar, 2018: Mapping Puerto Rico’s Hurricane Migration

With Mobile Phone Data.Publisher name: Bloomberg City Lab URL: https://

www.bloomberg.com/news/articles/2018-05-11/where-puerto-rico-s-

residents-migrated-since-maria. Last accessed 3 September 2021.

Eckstein, D., V. Künzel and L. Schäfer, 2017: Global Climate Risk Index 2018.

Germanwatch eV, Bonn, Germany. London, UK. 93 pp.

Edmonds, D.A., R. L. Caldwell, E. S. Brondizio and S. M. O. Siani, 2020: Coastal

ﬂooding will disproportionately impact people on river deltas. Nat. Commun.,

11(1), 4741, doi:10.1038/s41467-020-18531-4.

Edwards, B., M. Gray and B. Hunter, 2015: The impact of drought on mental

health in rural and regional Australia. Soc. Indic. Res., 121(1), 177–194,

doi:10.1007/s11205-014-0638-2.

Efstratiou, A., J. Ongerth and P. Karanis, 2017: Waterborne transmission of

protozoan parasites: review of worldwide outbreaks – an update 2011–

2016. Water Res., 114, 14–22, doi:10.1016/j.watres.2017.01.036.

Eguiluz-Gracia, I., etal., 2020: The need for clean air: the way air pollution

and climate change affect allergic rhinitis and asthma. Allergy, 75(9), 2170–

2184, doi:10.1111/all.14177.

Eisenman, D., S. McCaffrey, I. Donatello and G. Marshal, 2015: An ecosystems

and vulnerable populations perspective on solastalgia and psychological

distress after a wildﬁre. EcoHealth, 12(4), 602–610.

Ekstrom, J.A. and L. Bedsworth, 2018: Adapting air quality management for a

changing climate: survey of local districts in California. J. Air Waste Manag.

Assoc., 68(9), 931–944, doi:10.1080/10962247.2018.1459325.

El-Zein, A. and F.N. Tonmoy, 2017: Nonlinearity, fuzziness and incommensurability

in indicator-based assessments of vulnerability to climate change: a new

mathematical framework. Ecol. Indic., 82, 82–93.

Ellis, N.R. and G.A. Albrecht, 2017: Climate change threats to family farmers’

sense of place and mental wellbeing: a case study from the Western

Australian Wheatbelt. Soc. Sci. Med., 175, 161–168, doi:10.1016/j.

socscimed.2017.01.009.

Emmerson, K.M., etal., 2021: Atmospheric modelling of grass pollen rupturing

mechanisms for thunderstorm asthma prediction. PLoS ONE, 16(4),

doi:10.1371/journal.pone.0249488.

Ensor, J., J. Forrester and N. Matin, 2018: Bringing rights into resilience: revealing

complexities of climate risks and social conﬂict. Disasters, 42, S287–S305.

Epstein, A., etal., 2020a: Drought and intimate partner violence towards women

in 19countries in sub-Saharan Africa during 2011–2018: apopulation-based

study. PLoS Med, 17(3), e1003064, doi:10.1371/journal.pmed.1003064.

Epstein, A., T. Benmarhnia and S.D. Weiser, 2020b: Drought and illness among

young children in Uganda, 2009–2012. Am. J. Trop. Med. Hyg., 102(3), 644–

648, doi:10.4269/ajtmh.19-0412.

Epstein, Y. and R. Yanovich, 2019: Heatstroke. N. Engl. J. Med., 380(25), 2449–

2459, doi:10.1056/NEJMra1810762.

Eriksen, S., A. Nightingale and H. Eakin, 2015: Reframing adaptation: the

political nature of climate change adaptation. Glob. Environ. Change, 35,

523–533.

Eriksen, S., etal., 2021: Adaptation interventions and their effect on vulnerability

in developing countries: help, hindrance or irrelevance? World Dev., 141,

doi:10.1016/j.worlddev.2020.105383.

Escobar, L.E., etal., 2015: A global map of suitability for coastal Vibrio cholerae

under current and future climate conditions. Acta Trop., 149, 202–211,

doi:10.1016/j.actatropica.2015.05.028.

Espejo, W., J. Celis, G. Chiang and P. Bahamonde, 2020: Environment

and COVID-19: pollutants, impacts, dissemination, management and

recommendations for facing future epidemic threats. Sci. Total Environ., 747,

doi:10.1016/j.scitotenv.2020.141314.

Estrada-Peña, A. and N. Fernández-Ruiz, 2020: A retrospective assessment

of temperature trends in Northern Europe reveals a deep impact on the

1141

Health, Wellbeing and the Changing Structure of Communities Chapter 7

life cycle of Ixodes ricinus (Acari: Ixodidae). Pathogens, 9(5), doi:10.3390/

pathogens9050345.

European Environmental Agency 2017: Climate Change, Impacts and

Vulnerability in Europe 2016: An Indicator-based Report. Publications ofﬁce

of the European Union, Luexembourg. 419 pp.

European Food Safety Authority (EFSA), 2020: Climate Change as a Driver

of Emerging Risks for Food and Feed Safety, Plant, Animal Health and

Nutritional Quality. EFSA (European Food Safety Authority), 1–146.

doi:10.2903/sp.efsa.2020.EN-1881

Evangeliou, N., etal., 2014: Wildﬁres in Chernobyl-contaminated forests and

risks to the population and the environment: a new nuclear disaster about

to happen? Environ. Int., 73, 346–358, doi:10.1016/j.envint.2014.08.012.

Evans, G., 2019: Projected behavioral impacts of global climate change. Annu.

Rev. Psychol., 70, 449–474, doi:10.1146/annurev-psych-010418-103023.

Evertsen, K. and K. Van Der Geest, 2019: Gender, environment and migration in

Bangladesh. Clim. Dev., doi:10.1080/17565529.2019.1596059.

Ezeh, A., F. Kissling and P. Singer, 2020: Why sub-Saharan Africa might exceed

its projected population size by 2100. Lancet, 396(10258), 1131–1133,

doi:10.1016/s0140-6736(20)31522-1.

Fan, J. and Q. Liu, 2019: Potential impacts of climate change on dengue fever

distribution using RCP scenarios in China. Adv. Clim. Change Res., 10(1),

1–8, doi:10.1016/j.accre.2019.03.006.

Fan, Y., etal., 2021: Molecular characterization of the waterborne pathogens

Cryptosporidium spp., Giardia duodenalis, Enterocytozoon bieneusi,

Cyclospora cayetanensis and Eimeria spp. in wastewater and sewage in

Guangzhou, China. Parasit Vectors, 14(1), 66, doi:10.1186/s13071-020-

04566-5.

Fanzo, J., etal., 2020: Nutrients, foods, diets, people: promoting healthy eating.

Curr. Dev. Nutr., 4(6), doi:10.1093/cdn/nzaa069.

FAO, 2020: Climate Change: Unpacking the Burden on Food Safety. Food Safety

and Quality Series. Food and Agriculture Organization of the United Nations,

Rome, Italy.

FAO and Alliance of Biodiversity International and CIAT, 2021: Indigenous

Peoples’ Food Systems: Insights on Sustainability and Resilience from the

Front Line of Climate Change. FAO, Rome

FAO, etal., 2018: The State of Food Security and Nutrition in the World 2018.

Building Climate Resilience for Food Security and Nutrition. Policy Support

and Governance, Fao, Rome, http://www.fao.org/3/I9553EN/i9553en.pdf.

Last accessed 3 September 2021.

FAO, etal., 2020: The State of Food Security and Nutrition in the World 2020.

Transforming Food Systems for Affordable Healthy Diets. Fao, Rome, http://

www.fao.org/3/ca9692en/ca9692en.pdf. Last accessed 3 September 2021.

FAO, et al., 2021: The State of Food Security and Nutrition in the World

2021.Transforming Food Systems for Food Security, Improved Nutrition and

Affordable Healthy Diets for All. FAO, Rome.

FAO and WHO, 2018: Evaluation of Certain Veterinary Drug Residues in Food:

Eighty-Fifth Report of the Joint FAO/WHO Expert Committee on Food

Additives. World Health Organization, Geneva, Switzerland, Vol. 1008,

1–150.

FAOLEX, 2021: Plant Protection (Damage by Goats) Law, 1950. https://www.

ecolex.org/details/legislation/plant-protection-damage-by-goats-law-1950-

lex-faoc014270/ Last accessed 3 September 2021.

Farbotko, C., et al., 2020: Relocation planning must address voluntary

immobility. Nat. Clim. Change, 10(8), 702–704, doi:10.1038/s41558-020-

0829-6.

Farbotko, C. and C. McMichael, 2019: Voluntary immobility and existential

security in a changing climate in the Paciﬁc. Asia Pac. Viewp., 60, 148–162,

doi:10.1111/apv.12231.

Farchi, S., etal., 2017: Meat consumption reduction in Italian regions: health

co-beneﬁts and decreases in GHG emissions. PLoS ONE, 12(8), doi:10.1371/

journal.pone.0182960.

Farhat, J.B., etal., 2018: Syrian refugees in Greece: experience with violence,

mental health status, and access to information during the journey and

while in Greece. BMC Med., 1–12. DOI: https://doi.org/10.1186/s12916-018-

1028-4

Fatehpanah, A., etal., 2020: Water safety in drought: an indigenous knowledge-

based qualitative study. J. Water Health, 18(5), 692–703, doi:10.2166/

wh.2020.141.

Fawcett, D., T. Pearce, J.D. Ford and L. Archer, 2017: Operationalizing longitudinal

approaches to climate change vulnerability assessment. Glob. Environ.

Change, 45, 79–88.

Fazey, I., etal., 2016: Past and future adaptation pathways. Clim. Dev., 8(1),

26–44, doi:10.1080/17565529.2014.989192.

Fei, L., etal., 2015: Population change of farmland rodent and the inﬂuences of

climate and cultivation factors in Fengxian District of Shanghai, China. Ying

Yong Sheng Tai Xue Bao J. Appl. Ecol., 26(2), 579–587.

Fenech, S., etal., 2019: Meteorological drivers and mortality associated with

O-3 and PM2.5 air pollution episodes in the UK in 2006. Atmosph. Environ.,

213, 699–710, doi:10.1016/j.atmosenv.2019.06.030.

Ferkol, T. and D. Schraufnagel, 2014: The global burden of respiratory disease. Ann.

Am. Thorac. Soc., 11(3), 404–406, doi:10.1513/AnnalsATS.201311-405 PS.

Fernandez, A., etal., 2015: Flooding and mental health: a systematic mapping

review. PLoS ONE, 10(4), doi:10.1371/journal.pone.0119929.

Ferreira-Coimbra, J., C. Sarda and J. Rello, 2020: Burden of community-acquired

pneumonia and unmet clinical needs. Adv. Ther., 37(4), 1302–1318,

doi:10.1007/s12325-020-01248-7.

Ferreira, D.C., I. Graziele, R.C. Marques and J. Gonçalves, 2021: Investment in

drinking water and sanitation infrastructure and its impact on waterborne

diseases dissemination: the Brazilian case. Sci. Total Environ., 779, 146279,

doi:10.1016/j.scitotenv.2021.146279.

Flahault, A., R.R. de Castaneda and I. Bolon, 2016: Climate change and

infectious diseases. Public Health Rev., 37, doi:10.1186/s40985-016-0035-2.

Flatø, M., R. Muttarak and A. Pelser, 2017: Women, weather, and woes: the

triangular dynamics of female-headed households, economic vulnerability,

and climate variability in South Africa. World Dev., 90, 41–62.

Fogg-Rogers, L., etal., 2021: Applying social learning to climate communications

– visualising ‘people like me’ in air pollution and climate change data.

Sustainability, 13(6), doi:10.3390/su13063406.

Folkerts, M. A., etal., 2020: Long term adaptation to heat stress: shifts in the

minimum mortality temperature in the Netherlands. Front. Physiol., 11,

doi:10.3389/fphys.2020.00225.

Follos, F., etal., 2021: Evolution of the minimum mortality temperature (1983–

2018): Is Spain adapting to heat? Sci. Total Environ., 784, doi:10.1016/j.

scitotenv.2021.147233.

Ford, J. and D. King, 2015: A framework for examining adaptation readiness.

Mitigation and Adaptation Strategies for Global Change, 20 pp. 505-526.

Ford, J., et al., 2020: The resilience of indigenous peoples to environmental

change. One Earth, 2(6), 532–543, doi:10.1016/j.oneear.2020.05.014.

Ford, J.D., etal., 2018: Preparing for the health impacts of climate change in

indigenous communities: the role of community-based adaptation. Glob.

Environ. Change, 49, 129–139, doi:10.1016/j.gloenvcha.2018.02.006.

Ford, T.E. and S. Hamner, 2018: A perspective on the global pandemic of

waterborne disease. Microb. Ecol., 76(1), 2–8, doi:10.1007/s00248-015-

0629-0.

Foreman, K.J., etal., 2018: Forecasting life expectancy, years of life lost, and

all-cause and cause-speciﬁc mortality for 250 causes of death: reference and

alternative scenarios for 2016–40 for 195countries and territories. Lancet,

392(10159), 2052–2090, doi:10.1016/s0140-6736(18)31694-5.

Foster, J., S.G. Hodder, J. Goodwin and G. Havenith, 2020: Occupational heat

stress and practical cooling solutions for healthcare and industry workers

during the COVID-19 pandemic. Ann. Work Expo. Health, 64(9), 915–922,

doi:10.1093/annweh/wxaa082.

Foudi, S., N. Osés-Eraso and I. Galarraga, 2017: The effect of ﬂooding on mental

health: lessons learned for building resilience. Water Resour. Res., 53(7),

5831–5844.

1142

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Fox, M., etal., 2019: Integrating public health into climate change policy and

planning: state of practice update. Int. J. Environ. Res. Public Health, 16(18),

doi:10.3390/ijerph16183232.

Froese, R. and J. Schilling, 2019: The nexus of climate change, land use, and

conﬂicts. Curr. Clim. Change Rep., 5(1), 24–35.

Fröhlich, C. and G. Gioli, 2015: Gender, conﬂict, and global environmental

change. Peace Rev. J. Soc. Justice, 27(2), 137–146, doi:10.1080/10402659

.2015.1037609.

Fröhlich, C., G. Gioli, R. Cremades and H. Myrttinen, 2018: Water Security Across

the Gender Divide. Springer, Dordrecht, ISBN 978-3319640464.

Fröhlich, C.J., 2016: Climate migrants as protestors? Dispelling misconceptions

about global environmental change in pre-revolutionary Syria. Contemp.

Levant, 1(1), 38–50.

Frumkin, H., 2018: The US health care sector’s carbon footprint: stomping

or treading lightly? Am. J. Public Health, 108(S2), S56–S57, doi:10.2105/

ajph.2017.304160.

Frumkin, H. and A. Haines, 2019: Global environmental change and

noncommunicable disease risks. Annu. Rev. Public Health, 40(1), 261–282,

doi:10.1146/annurev-publhealth-040218-043706.

FSIN, 2020: Global Report on Food Crises: Joint Analysis for Better Decisions.

Food and Agriculture Organization (FAO); World Food Programme (WFP);

and International Food Policy Research Institute (IFPRI), Rome, Italy and

Washington, DC

FSIN and GNAFC, 2020: Global Report on Food Crises 2020. Food Security

Information Network and Global Network Against Food Crises, FSIN/GNAFC,

Rome.

Fu, T.M. and H. Tian, 2019: Climate change penalty to ozone air quality: review

of current understandings and knowledge gaps. Curr. Pollut. Rep., 5(3), 159–

171, doi:10.1007/s40726-019-00115-6.

Fuentes, L., H. Asselin, A. Belisle and O. Labra, 2020: Impacts of environmental

changes on well-being in indigenous communities in eastern Canada. Int. J.

Environ. Res. Public Health, 17(2), 11, doi:10.3390/ijerph17020637.

Fujii, H., et al., 2015: Fatigue and sleep under large summer temperature

differences. Environ. Res., 138, 17–21, doi:10.1016/j.envres.2015.02.006.

Fullilove, M.T., 1996: Psychiatric implications of displacement: contributions

from the psychology of place. Am. J. Psychiatry, 153, 1516–1523.

Fullman, N., etal., 2017: Measuring progress and projecting attainment on the

basis of past trends of the health-related Sustainable Development Goals in

188countries: an analysis from the Global Burden of Disease Study 2016.

Lancet, 390(10100), 1423–1459, doi:10.1016/s0140-6736(17)32336-x.

Fummi, M., J. Andries and B. Yonas, 2017: The role of indigenous knowledge

in drought risk reduction: a case of communal farmers in South Africa. J.

Disaster Risk Stud., 9, 1–6, doi:10.4102/jamba.v9i1.420.

Funk, C., etal., 2018: Contrasting Kenyan Resilience to Drought: 2011 and

2017. US AID, https://www.usaid.gov/sites/default/ﬁles/documents/1867/

Kenya_Report_-_Full_Compliant.PDF . (Special Report ed., Aid, U. S., USA,

20 pp). Last accessed 3 September 2021.

Funk, C., etal., 2019: Recognizing the famine early warning systems network:

over 30years of drought early warning science advances and partnerships

promoting global food security. Bull. Am. Meteorol. Soc., 100(6), 1011–1027,

doi:10.1175/bams-d-17-0233.1.

Furberg, M., B. Evengard and M. Nilsson, 2011: Facing the limit of resilience:

perceptions of climate change among reindeer herding Sami in Sweden.

Glob. Health Action, 4, doi:10.3402/gha.v4i0.8417.

Furgal, C. and J. Seguin, 2006: Climate change, health, and vulnerability in

Canadian northern Aboriginal communities. Environ. Health Perspect.,

114(12), 1964–1970, doi:10.1289/ehp.8433.

Fussell, E., R. McLeman and F. Gemenne, 2018: Population displacements

and migration patterns in response to Hurricane Katrina. In: Handbook of

Environmental Displacement and Migration. London/ New York, Routledge,

pp. 277–288.

Gakidou et al., 2018b: Global, regional, and national comparative risk

assessment of 84 behavioural, environmental and occupational, and

metabolic risks or clusters of risks for 195countries and territories, 1990–

2017: a systematic analysis for the Global Burden of Disease Study 2017.

Lancet, 392(10159), 1923–1994, doi:10.1016/S0140-6736(18)32225-6.

Gamage, P.J., L.V. Fortington and C.F. Finch, 2020: Epidemiology of exertional

heat illnesses in organised sports: a systematic review. J. Sci. Med. Sport,

23(8), 701–709, doi:10.1016/j.jsams.2020.02.008.

Gandon, S., T. Day, C.J.E. Metcalf and B.T. Grenfell, 2016: Forecasting

epidemiological and evolutionary dynamics of infectious diseases. Trends

Ecol. Evol., 31(10), 776–788, doi:10.1016/j.tree.2016.07.010.

Gao, J., et al., 2018: Public health co-beneﬁts of greenhouse gas emissions

reduction: a systematic review. Sci. Total Environ., 627, 388–402,

doi:10.1016/j.scitotenv.2018.01.193.

Garcia, D.M. and M.C. Sheehan, 2016: Extreme weather-driven

disasters and children’s health. Int. J. Health Serv., 46(1), 79–105,

doi:10.1177/0020731415625254.

Gari, T., etal., 2017: Anaemia among children in a drought affected community

in south-central Ethiopia. PLoS ONE, 12(3), e170898, doi:10.1371/journal.

pone.0170898.

Gariepy, G., H. Honkaniemi and A. Quesnel-Vallée, 2016: Social support and

protection from depression: systematic review of current ﬁndings in Western

countries. Br. J. Psychiatry, 209, doi:10.1192/bjp.bp.115.169094.

Garnier, J., etal., 2020: Helping to heal nature and ourselves through human-

rights-based and gender-responsive One Health. One Health Outlook, 2(1),

22, doi:10.1186/s42522-020-00029-0.

Garrido-Perez, J.M., C. Ordonez, R. Garcia-Herrera and J.L. Schnell, 2019: The

differing impact of air stagnation on summer ozone across Europe. Atmosp.

Environ., 219, doi:10.1016/j.atmosenv.2019.117062.

Gascon, M., etal., 2019: Correlates of walking for travel in seven European

cities: the PASTA project. Environ. Health Perspect., 127(9), doi:10.1289/

ehp4603.

Gaskin, C., etal., 2017: Factors associated with the climate change vulnerability

and the adaptive capacity of people with disability: a systematic review.

Weather. Clim. Soc., 9(4), 801–814, doi:10.1175/wcas-d-16-0126.1.

Gasmi, S., etal., 2017: Surveillance for Lyme disease in Canada: 2009–2015.

Can. Commun. Dis. Rep., 43(10), 194–199, doi:10.14745/ccdr.v43i10a01.

Gasparrini, A., Y. Guo and F. Sera, 2017: Projections of temperature-related

excess mortality under climate change scenarios. Lancet Planet. Health, 1(9),

e360–e367.

Gasparrini, A., etal., 2015: Mortality risk attributable to high and low ambient

temperature: a multicountry observational study. Lancet, 386(9991), 369–

375, doi:10.1016/s0140-6736(14)62114-0.

Gautier, D., D. Denis and B. Locatelli, 2016: Impacts of drought and responses of

rural populations in West Africa: a systematic review. WIREs Clim. Change,

7(5), 666–681.

Gebreslassie, M., 2020: COVID-19 and energy access: an opportunity or a

challenge for the African continent? Energy Res. Soc. Sci., 68, doi:10.1016/j.

erss.2020.101677.

Geier, D.A., J.K. Kern and M.R. Geier, 2018: A longitudinal ecological study of

seasonal inﬂuenza deaths in relation to climate conditions in the United

States from 1999 through 2011. Infect. Ecol. Epidemiol., 8, doi:10.1080/200

08686.2018.1474708.

Gemenne, F. and J. Blocher, 2017: How can migration serve adaptation to

climate change? Challenges to ﬂeshing out a policy ideal. Geogr. J., 183(4),

336–347, doi:10.1111/geoj.12205.

Ghazani, M., et al., 2018: Temperature variability and gastrointestinal

infections: a review of impacts and future perspectives. Int. J. Environ. Res.

Public Health, 15(4), 16, doi:10.3390/ijerph15040766. Electronic Resource.

Ghimire, R., S. Ferreira and J.H. Dorfman, 2015: Flood-induced displacement and

civil conﬂict. World Dev., 66, 614–628, doi:10.1016/j.worlddev.2014.09.021.

Ghimire, S. and S. Dhakal, 2015: Japanese encephalitis: challenges and

intervention opportunities in Nepal. Vet. World, 8(1), 61–65, doi:10.14202/

vetworld.2015.61-65.

1143

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Gibb, R., etal., 2020: Zoonotic host diversity increases in human-dominated

ecosystems. Nature, 584(7821), 398, doi:10.1038/s41586-020-2562-8.

Gibon, T., et al., 2017: Health beneﬁts, ecological threats of low-carbon

electricity. Environ. Res. Lett., 12(3), 34023, doi:10.1088/1748-9326/aa6047.

Gibson, K., J. Barnett, N. Haslam and I. Kaplan, 2020: The mental health impacts

of climate change: ﬁndings from a Paciﬁc island atoll nation. J. Anxiety

Disord., 73, doi:10.1016/j.janxdis.2020.102237.

Gilmore, E.A. and H. Buhaug, 2021: Climate mitigation policies and the potential

pathways to conﬂict: outlining a research agenda. Wiley Interdiscip. Rev.

Clim. Change, 12 (5), e722.

Gilmore, E.A., L.H. Risi, E. Tennant and H. Buhaug, 2018: Bridging research

and policy on climate change and conﬂict. Curr. Clim. Change Rep., 4(4),

313–319.

Giorgini, P., et al., 2017: Climate changes and human health: a review of

the effect of environmental stressors on cardiovascular diseases across

epidemiology and biological mechanisms. Curr. Pharm. Des., 23(22), 3247–

3261, doi:10.2174/1381612823666170317143248.

Gleick, P.H., 2014: Water, drought, climate change, and conﬂict in Syria. Weather

Clim. Soc., 6(3), 331–340, doi:10.1175/wcas-d-13-00059.1.

Global Nutrition Report 2020: Global Nutrition Report: Action on Equity to End

Malnutrition. Development Initiatives, Bristol, UK.

Glover, D. and N. Poole, 2019: Principles of innovation to build nutrition-

sensitive food systems in South Asia. Food Policy, 82, 63–73, doi:10.1016/j.

foodpol.2018.10.010.

Gnonlonﬁn, G.J.B., etal., 2013: A review on aﬂatoxin contamination and its

implications in the developing world: a sub-Saharan African perspective.

Crit. Rev. Food Sci. Nutr., 53(4), 349–365, doi:10.1080/10408398.2010.53

5718.

GNR, 2018: Global Nutrition Report: Shining a Light to Spur Action on Nutrition.

Development Initiatives, Bristol, UK.

Gobler, C.J., etal., 2017: Ocean warming since 1982 has expanded the niche of

toxic algal blooms in the North Atlantic and North Paciﬁc oceans. Proc. Natl.

Acad. Sci., 114(19), 4975, doi:10.1073/pnas.1619575114.

Goggins, W.B. and E.Y.Y. Chan, 2017: A study of the short-term associations

between hospital admissions and mortality from heart failure and

meteorological variables in Hong Kong: weather and heart failure in Hong

Kong. Int. J. Cardiol., 228, 537–542, doi:10.1016/j.ijcard.2016.11.106.

Goodrich, C., A. Prakash and P. Udas, 2019: Gendered vulnerability and

adaptation in Hindu-Kush Himalayas: research insights. Environ. Dev., 31,

1–8, doi:10.1016/j.envdev.2019.01.001.

Gordon, K., A.L. Xavier and H.T. Neufeld, 2018: Healthy roots: building capacity

through shared stories rooted in Haudenosaunee knowledge to promote

indigenous foodways and well-being. Can. Food Stud., 52(2), 180–195.

Gosling, S.N., etal., 2017: Adaptation to climate change: a comparative analysis

of modeling methods for heat-related mortality. Environ. Health Perspect.,

125(8), doi:10.1289/ehp634.

Goyal, M., etal., 2021: The effect of the COVID-19 pandemic on maternal health

due to delay in seeking health care: experience from a tertiary center. Int. J.

Gynecol. Obstet., 152(2), 231–235, doi:10.1002/ijgo.13457.

Gray, C., D. Hopping and V. Mueller, 2020: The changing climate-migration

relationship in China, 1989–2011. Clim. Change, doi:10.1007/s10584-020-

02657-x.

Gray, C. and E. Wise, 2016: Country-speciﬁc effects of climate variability on

human migration. Clim. Change, 135(3-4), 555–568, doi:10.1007/s10584-

015-1592-y.

Green, D. and L. Minchin, 2014: Living on climate-changed country: indigenous

health, well-being and climate change in remote Australian communities.

EcoHealth, 11(2), 263–272, doi:10.1007/s10393-013-0892-9.

Green, D., etal., 2017: Advancing Australia’s role in climate change and health

research. Nat. Clim. Change, 7(2), 103–106, doi:10.1038/nclimate3182.

Green, H., etal., 2019: Impact of heat on mortality and morbidity in low and

middle income countries: a review of the epidemiological evidence and

considerations for future research. Environ. Res., 171, 80–91, doi:10.1016/j.

envres.2019.01.010.

Green, R., etal., 2015: The potential to reduce greenhouse gas emissions in the

UK through healthy and realistic dietary change. Climatic Change, 129(1),

253–265, doi:10.1007/s10584-015-1329-y.

Greenwood, M. and N. Lindsay, 2019: A commentary on land, health,

and Indigenous knowledge(s). Glob. Health Promot., 26(3), 82–86,

doi:10.1177/1757975919831262.

Grey, S. and R. Patel, 2015: Food sovereignty as decolonization: some

contributions from Indigenous movements to food system and development

politics. Agric. Hum. Values, 32(3), 431–444, doi:10.1007/s10460-014-9548-

Grundstein, A., etal., 2017: American football and fatal exertional heat stroke:

a case study of Korey Stringer. Int. J. Biometeorol., 61(8), 1471–1480,

doi:10.1007/s00484-017-1324-2.

Guetter, C.R., etal., 2018: Greening the operating room. Am. J. Surg., 216(4),

683–688, doi:10.1016/j.amjsurg.2018.07.021.

Guirguis, K., et al., 2018: Heat, disparities, and health outcomes in San

Diego county’s diverse climate zones. GeoHealth, 2(7), 212–223,

doi:10.1029/2017gh000127.

Guo, Y., et al., 2020: The origin, transmission and clinical therapies on

coronavirus disease 2019 (COVID-19) outbreak – an update on the status.

Mil. Med. Res., 7(1), doi:10.1186/s40779-020-00240-0.

Guo, Y., et al., 2016: Temperature variability and mortality: a multi-country

study. Environ. Health Perspect., 124(10), 1554–1559, doi:10.1289/ehp149.

Guo, Y., etal., 2018: Quantifying excess deaths related to heatwaves under

climate change scenarios: a multicountry time series modelling study. PLoS

Med., 15(7), e1002629, doi:10.1371/journal.pmed.1002629.

Guo, Y.M., 2017: Hourly associations between heat and ambulance calls.

Environ. Pollut., 220, 1424–1428, doi:10.1016/j.envpol.2016.10.091.

Guyot, M., etal., 2006: Local observations of climate change and impacts on

traditional food security in two northern Aboriginal communities. Int. J.

Circumpolar Health, 65(5), 403–415, doi:10.3402/ijch.v65i5.18135.

Guzman Herrador, B., etal., 2016: Association between heavy precipitation

events and waterborne outbreaks in four Nordic countries, 1992–2012. J.

Water Health, 14(6), 1019–1027, doi:10.2166/wh.2016.071.

Guzman Herrador, B.R., etal., 2015: Analytical studies assessing the association

between extreme precipitation or temperature and drinking water-related

waterborne infections: a review. Environ. Health, 14, 29, doi:10.1186/

s12940-015-0014-y.

Haas, S., A. Gianoli and M. Van Eerd, 2021: The roles of community

resilience and risk appraisal in climate change adaptation: the experience

of the Kannagi Nagar resettlement in Chennai. Environ. Urban.,

doi:10.1177/0956247821993391.

Haasnoot, M., J. Lawrence and A.K. Magnan, 2021: Pathways to coastal

retreat. Science, 372(6548), 1287–1290. http://science.sciencemag.org/

content/372/6548/1287.abstract.

Habibi, P., etal., 2021: The impacts of climate change on occupational heat strain

in outdoor workers: a systematic review. Urban Clim., 36, doi:10.1016/j.

uclim.2021.100770.

Haddout, S., K.L. Priya, A.M. Hoguane and I. Ljubenkov, 2020: Water scarcity:

a big challenge to slums in Africa to ﬁght against COVID-19. Sci. Technol.

Libr., 39(3), 1–8.

Haghighi, M.M., etal., 2021: Impacts of high environmental temperatures on

congenital anomalies: a systematic review. Int. J. Environ. Res. Public Health,

18(9), doi:10.3390/ijerph18094910.

Haines, A. and K. Ebi, 2019: The imperative for climate action to protect health.

N. Engl. J. Med., 380(3), 263–273, doi:10.1056/NEJMra1807873.

Hajat, S., 2017: Health effects of milder winters: a review of evidence from the

United Kingdom. Environ. Health, 16, 109, doi:10.1186/s12940-017-0323-4.

Hajat, S., etal., 2017: The effect of ambient temperature on type-2-diabetes:

case-crossover analysis of 4+ million GP consultations across England.

Environ. Health, 16(1), 73, doi:10.1186/s12940-017-0284-7.

1144

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Hall, N., etal., 2021: Climate change and infectious diseases in Australia’s Torres

Strait Islands. Aust. N. Z. J. Public Health, 45(2), 122–128, doi:10.1111/1753-

6405.13073.

Hall, N. and L. Crosby, 2020: Climate change impacts on health in remote

indigenous communities in Australia. Int. J. Environ. Health Res, 16, doi:10.1

080/09603123.2020.1777948.

Hallema, D., F. Robinne and S. McNulty, 2020: Pandemic spotlight on urban

water quality. Ecol. Process., 9(1), doi:10.1186/s13717-020-00231-y.

Hamaoui-Laguel, L., etal., 2015: Effects of climate change and seed dispersal

on airborne ragweed pollen loads in Europe. Nat. Clim. Change, 5, 766,

doi:10.1038/nclimate2652.

Hamilton, I., etal., 2021: The public health implications of the Paris Agreement:

a modelling study. Lancet Planet. Health, 5(2), E74–E83.

Hamilton, L.C., K. Saito and P.A. Loring, 2016: Climigration? Population and

climate change in Arctic Alaska, Population and environment, 38(2), 115–

133.

Hanigan, I., C. Butler, P. Kokic and M. Hutchinson, 2012: Suicide and drought

in New South Wales, Australia, 1970–2007. Proc. Natl. Acad. Sci. U.S.A.,

109(35), 13950–13955, doi:10.1073/pnas.1112965109.

Hanigan, I.C., K.B.G. Dear and A. Woodward, 2021: Increased ratio of summer

to winter deaths due to climate warming in Australia, 1968–2018. Aust. N.

Z. J. Public Health, doi:10.1111/1753-6405.13107.

Hansel, N.N., M.C. McCormack and V. Kim, 2016: The effects of air pollution

and temperature on COPD. COPD, 13(3), 372–379, doi:10.3109/15412555

.2015.1089846.

Hansen, A., et al., 2008: The effect of heat waves on mental health in a

temperate Australian city. Environ. Health Perspect., 116(10), 1369–1375,

doi:10.1289/ehp.11339.

Hansen, A., et al., 2015: Transmission of haemorrhagic fever with renal

syndrome in china and the role of climate factors: a review. Int. J. Infect. Dis.,

33, 212–218, doi:10.1016/j.ijid.2015.02.010.

Haq, G. and G. Gutman, 2014: Climate gerontology meeting the challenge

of population ageing and climate change. Z. Gerontol. Geriat., 47(6), 462,

doi:10.1007/s00391-014-0677-y.

Haque, M., et al., 2015: Behavioral change due to climate change effects

accelerate tiger human conﬂicts: a study on Sundarbans mangrove forests,

Bangladesh. Int. J. Conserv. Sci., 6(4), 669–684.

Haque, M.R., N. Parr and S. Muhidin, 2019: Parents’ healthcare-seeking

behavior for their children among the climate-related displaced

population of rural Bangladesh. Soc. Sci. Med., 226, 9–20, doi:10.1016/j.

socscimed.2019.02.032.

Harder, R., M. Giampietro and S. Smukler, 2021: Towards a circular nutrient

economy. A novel way to analyze the circularity of nutrient ﬂows in food

systems. Resour. Conserv. Recycl., 172, doi:10.1016/j.resconrec.2021.105693.

Harper, S., et al., 2015: Climate-sensitive health priorities in Nunatsiavut,

Canada. BMC Public Health, 15, doi:10.1186/s12889-015-1874-3.

Harrington, L. and F. Otto, 2018: Changing population dynamics and uneven

temperature emergence combine to exacerbate regional exposure to

heat extremes under 1.5°C and 2°C of warming. Environ. Res. Lett., 13,

doi:10.1088/1748-9326/aaaa99.

Harrington, L.J., D.J. Frame, E. Hawkins and M. Joshi, 2017: Seasonal cycles

enhance disparities between low- and high-income countries in exposure to

monthly temperature emergence with future warming. Environ. Res. Lett.,

12(11), doi:10.1088/1748-9326/aa95ae.

Hasegawa, T., etal., 2018: Risk of increased food insecurity under stringent

global climate change mitigation policy. Nat. Clim. Change, 8(8), 699–703,

doi:10.1038/s41558-018-0230-x.

Hasegawa, T., S. Fujimori, K. Takahashi and T. Masui, 2015: Scenarios for the risk

of hunger in the twenty-ﬁrst century using Shared Socioeconomic Pathways.

Environ. Res. Lett., 10(1), doi:10.1088/1748-9326/10/1/014010.

Hashim, J.H. and Z. Hashim, 2016: Climate change, extreme weather events,

and human health implications in the Asia Paciﬁc region. Asia Pac. J. Public

Health, 28(2 Suppl), 8S–14S, doi:10.1177/1010539515599030.

Hassan, E.M., etal., 2021: A review of Cryptosporidium spp. and their detection

in water. Water Sci. Technol., 83(1), 1–25, doi:10.2166/wst.2020.515.

Hatﬁeld, S., etal., 2018: Indian time: time, seasonality, and culture in traditional

ecological knowledge of climate change. Ecol. Process., 7, doi:10.1186/

s13717-018-0136-6.

Hatvani-Kovacs, G., J. Bush, E. Shariﬁ and J. Boland, 2018: Policy recommendations

to increase urban heat stress resilience. Urban Clim., 25, 51–63.

Hauer, M. E., 2017: Migration induced by sea-level rise could reshape the

US population landscape. Nat. Clim. Change, 7(5), 321, doi:10.1038/

nclimate3271.

Hauer, M. E., J.M. Evans and D.R. Mishra, 2016: Millions projected to be at risk

from sea-level rise in the continental United States. Nat. Clim. Change, 6,

691–695.

Hauer, M. E., R.D. Hardy, D.R. Mishra and J.S. Pippin, 2019: No landward

movement: examining 80 years of population migration and shoreline

change in Louisiana. Popul. Environ, 40(4), 369-387.

Haussig, J.M., et al., 2018: Early start of the West Nile fever transmission

season 2018 in Europe. Euro Surveill., 23(32), 7–12, doi:10.2807/1560-7917.

es.2018.23.32.1800428.

Hayashida, K., K. Shimizu and H. Yokota, 2019: Severe heatwave in Japan.

Acute Med. Surg., 6(2), 206–207, doi:10.1002/ams2.387.

Hayek, M., H. Harwatt, W. Ripple and N. Mueller, 2021: The carbon opportunity

cost of animal-sourced food production on land. Nat. Sustain., 4(1), 21–24,

doi:10.1038/s41893-020-00603-4.

Hayes, K., P. Berry and K. Ebi, 2019: Factors inﬂuencing the mental health

consequences of climate change in Canada. Int. J. Environ. Res. Public

Health, 16, 1583, doi:10.3390/ijerph16091583.

Hayes, K., etal., 2018: Climate change and mental health: risks, impacts and

priority actions. Int. J. Ment. Health Syst., 12, 1–12, doi:10.1186/s13033-

018-0210-6.

Hayes, K. and B. Poland, 2018: Addressing mental health in a changing climate:

incorporating mental health indicators into climate change and health

vulnerability and adaptation assessments. Int. J. Environ. Res. Public Health,

15(9), doi:10.3390/ijerph15091806.

Hayward, G. and S. Ayeb-Karlsson, 2021: ‘Seeing with Empty Eyes’: a systems

approach to understand climate change and mental health in Bangladesh.

Clim. Change, 165(1), 1–30.

Heaney, A. and S. Winter, 2016: Climate-driven migration: an exploratory case

study of Maasai health perceptions and help-seeking behaviors. Int. J. Public

Health, 61(6), 641–649, doi:10.1007/s00038-015-0759-7.

Heaviside, C., S. Vardoulakis and X.M. Cai, 2016: Attribution of mortality to

the urban heat island during heatwaves in the West Midlands, UK. Environ.

Health, 15, doi:10.1186/s12940-016-0100-9.

Hedenus, F., S. Wirsenius and D.J. Johansson, 2014: The importance of reduced

meat and dairy consumption for meeting stringent climate change targets.

Climatic change, 124(1), 79–91, doi:10.1007/s10584-014-1104-5.

Hegre, H., etal., 2016: Forecasting civil conﬂict along the shared socioeconomic

pathways. Environ. Res. Lett., 11(5), 54002.

Hegre, H., H.M. Nygård and P. Landsverk, 2021: Can we predict armed conﬂict?

How the ﬁrst 9years of published forecasts stand up to reality. Int. Stud. Q.

65(3), 660-668.

Hegre, H. and N. Sambanis, 2006: Sensitivity analysis of empirical results on

civil war onset. J. Conﬂ. Resolut., 50(4), 508–535.

Heilmann, K., M. Kahn and C. Tang, 2021: The urban crime and heat gradient

in high and low poverty areas. J .Public Econ., 197, doi:10.1016/j.

jpubeco.2021.104408.

Heininen, L. and H. Exner-Pirot, 2020: Climate Change and Arctic Security.

Springer International Publishing.

Hellberg, R.S. and E. Chu, 2016: Effects of climate change on the persistence

and dispersal of foodborne bacterial pathogens in the outdoor environment:

a review. Crit. Rev. Microbiol., 42(4), 548–572, doi:10.3109/104084

1x.2014.972335.

1145

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Hellden, D., etal., 2021: Climate change and child health: a scoping review

and an expanded conceptual framework. Lancet Planet. Health, 5(3), E164–

E175.

Helm, S., et al., 2018: Differentiating environmental concern in the context

of psychological adaption to climate change. Glob. Environ. Change Policy

Dimens., 48, 158–167, doi:10.1016/j.gloenvcha.2017.11.012.

Hendrix, C. and S. Haggard, 2015: Global food prices, regime type, and

urban unrest in the developing world. J. Peace Res., 52(2), 143–157,

doi:10.1177/0022343314561599.

Henrique, K. and P. Tschakert, 2020: Pathways to urban transformation:

from dispossession to climate justice. Prog. Hum. Geogr.,

doi:10.1177/0309132520962856.

Heo, S., M.L. Bell and J.T. Lee, 2019a: Comparison of health risks by heat wave

deﬁnition: applicability of wet-bulb globe temperature for heat wave criteria.

Environ. Res., 168, 158–170, doi:10.1016/j.envres.2018.09.032.

Heo, S., etal., 2019b: The use of a quasi-experimental study on the mortality

effect of a heat wave warning system in Korea. Int. J. Environ. Res. Public

Health, 16(12), doi:10.3390/ijerph16122245.

Hepburn, C., etal., 2020: Will COVID-19 ﬁscal recovery packages accelerate or

retard progress on climate change? Oxf. Rev. Econ. Policy, 36, 359–S381,

doi:10.1093/oxrep/graa015.

Hermans, K. and L. Garbe, 2019: Droughts, livelihoods, and human migration in

northern Ethiopia. Reg. Environ. Change, doi:10.1007/s10113-019-01473-z.

Heslin, A., 2019: Climate migration and cultural preservation: the case of the

Marshallese diaspora. In: Loss and Damage from Climate Change: Concepts,

Methods and Policy Options. Springer, Cham. ISBN 978-3319720265.

Heslin, A., 2021: Riots and resources: how food access affects collective

violence. J. Peace Res., 58(2), 199–214.

Hess, J., J. McDowell and G. Luber, 2012: Integrating climate change adaptation

into public health practice: using adaptive management to increase adaptive

capacity and build resilience. Environ. Health Perspect., 120(2), 171–179,

doi:10.1289/ehp.1103515.

Hillier, S.A., A. Taleb, E. Chaccour and C. Aenishaenslin, 2021: Examining the

concept of One Healthfor indigenous communities: a systematic review.

One Health, 12, 100248, doi:10.1016/j.onehlt.2021.100248.

Hino, M., C.B. Field and K.J. Mach, 2017: Managed retreat as a response to

natural hazard risk. Nat. Clim. Change, 7, 364–370.

HLPE, 2020: Food Security and Nutrition: Building a Global narrative Towards

2030. FAO, Rome.

Hobbie, S.E. and N.B. Grimm, 2020: Nature-based approaches to managing

climate change impacts in cities. Philos. Trans. Royal Soc. B Biol. Sci.,

375(1794), doi:10.1098/rstb.2019.0124.

Hochman, A., etal., 2021: The relationship between cyclonic weather regimes

and seasonal inﬂuenza over the Eastern Mediterranean. Sci. Total Environ.,

750, doi:10.1016/j.scitotenv.2020.141686.

Hock, S., et al., 2018: Childhood malnutrition and maltreatment are linked

with personality disorder symptoms in adulthood: Results from a Barbados

lifespan cohort. Psychiatry Res., 269, 301–308.

Høeg, E. and C. Tulloch, 2018: Sinking strangers: media representations

of climate refugees on the BBC and Al Jazeera. J. Commun. Inq., 43,

19685991880948, doi:10.1177/0196859918809486.

Hoffman, J., V. Shandas and N. Pendleton, 2020: The effects of historical housing

policies on resident exposure to intra-urban heat: a study of 108 US urban

areas. Climate, 8(1), doi:10.3390/cli8010012.

Hoffmann, R., et al., 2020: A meta-analysis of country-level studies on

environmental change and migration. Nat. Clim. Change, 10(10), 904–912,

doi:10.1038/s41558-020-0898-6.

Hollowed, A., etal., 2013: Projected impacts of climate change on marine ﬁsh

and ﬁsheries. Ices J. Mar. Sci., 70(5), 1023–1037, doi:10.1093/icesjms/fst081.

Holman, C., R.M. Harrison and X. Querol, 2015: Review of the efﬁcacy of low

emission zones to improve urban air quality in European cities. Atmos.

Environ., 111, 161–169, doi:10.1016/j.atmosenv.2015.04.009.

Holsman, K., etal., 2020: Ecosystem-based ﬁsheries management forestalls

climate-driven collapse. Nat. Commun., 11(1), doi:10.1038/s41467-020-

18300-3.

Honda, Y. and D. Onozuka, 2020: Heat-Related Mortality/Morbidity in East Asia.

In: Extreme Weather Events and Human Health: International Case Studies,

pp. 131–144. Springer, Dordrecht. doi:10.1007/978-3-030-23773-8_10.

Hondula, D.M., M. Georgescu and R.C. Balling, 2014: Challenges associated

with projecting urbanization-induced heat-related mortality. Sci. Total

Environ., 490, 538–544, doi:10.1016/j.scitotenv.2014.04.130.

Hong, C.P., etal., 2019: Impacts of climate change on future air quality and

human health in China. Proc. Natl. Acad. Sci. U.S.A., 116(35), 17193–17200,

doi:10.1073/pnas.1812881116.

Hooijer, A. and R. Vernimmen, 2021: Global LiDAR land elevation data reveal

greatest sea-level rise vulnerability in the tropics. Nat. Commun., 12(1),

3592, doi:10.1038/s41467-021-23810-9.

Hopp, S., F. Dominici and J.F. Bobb, 2018: Medical diagnoses of heat wave-

related hospital admissions in older adults. Prev. Med., 110, 81–85,

doi:10.1016/j.ypmed.2018.02.001.

Horn, L.M., etal., 2018: Association between precipitation and diarrheal disease

in Mozambique. Int. J. Environ. Res. Public Health, 15(4), doi:10.3390/

ijerph15040709.

Hornsey, M., E. Harris, P. Bain and K. Fielding, 2016: Meta-analyses of the

determinants and outcomes of belief in climate change. Nat. Clim. Change,

6(6), 622, doi:10.1038/nclimate2943.

Horton, R.M., A.W. de Sherbinin, D.Wrathall and M. Oppenheimer, 2021:

Assessing human habitability and migration. Science 372(6548), 1279–

1283. Available at: http://science.sciencemag.org/content/372/6548/1279.

abstract.

Hossain, K., 2016: Securitizing the Arctic indigenous peoples: a community

security perspective with special reference to the Sámi of the European high

north. Polar Sci., 10(3), 415–424.

Houde, M., et al., 2020: Mercury in ringed seals (Pusa hispida) from the

Canadian Arctic in relation to time and climate parameters. Environ. Toxicol.

Chem., 39(12), 2462–2474, doi:10.1002/etc.4865.

Houéménou, H., etal., 2021: Pathogenic Leptospira and water quality in African

cities: a case study of Cotonou, Benin. Sci. Total Environ., 774, 145541,

doi:10.1016/j.scitotenv.2021.145541.

Howard, C., et al., 2021: SOS! Summer of Smoke: a retrospective cohort

study examining the cardiorespiratory impacts of a severe and prolonged

wildﬁre season in Canada’s high subarctic. BMJ Open, 11(2), doi:10.1136/

bmjopen-2020-037029.

Howard, G., R. Calow, A. Macdonald and J. Bartram, 2016: Climate change

and water and sanitation: likely impacts and emerging trends for

action. Annu. Rev. Environ. Resour., 41, 253–276, doi:10.1146/annurev-

environ-110615-085856.

Hoy, D., etal., 2014: Adapting to the health impacts of climate change in a

sustainable manner. Glob. Health, 10, doi:10.1186/s12992-014-0082-8.

Hrabok, M., A. Delorme and V.I. Agyapong, 2020: Threats to mental health and

well-being associated with climate change. J. Anxiety Disord., 76, 102295.

Hsiang, S., M. Burke and E. Miguel, 2013: Quantifying the inﬂuence of climate

on human conﬂict. Science, 341(6151), 1212, doi:10.1126/science.1235367.

Hsiang, S., etal., 2017: Estimating economic damage from climate change in

the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.

aal4369.

Hsiang, S.M. and B. Marshall, 2014: Climate, conﬂict, and social stability: what

does the evidence say? Clim. Change, 123(1), 39–55, doi:10.1007/s10584-

013-0868-3.

Hu, L.Q., O.V. Wilhelmi and C. Uejio, 2019: Assessment of heat exposure in

cities: combining the dynamics of temperature and population. Sci. Total

Environ., 655, 1–12, doi:10.1016/j.scitotenv.2018.11.028.

Huang, C., etal., 2018: Mortality burden attributable to heatwaves in Thailand:

a systematic assessment incorporating evidence-based lag structure.

Environ. Int., 121, 41–50, doi:10.1016/j.envint.2018.08.058.

1146

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Huang, J., etal., 2018: Projections for temperature-related years of life lost

from cardiovascular diseases in the elderly in a Chinese city with typical

subtropical climate. Environ. Res., 167, 614–621.

Huber, R., etal., 2020a: Symposium report: emerging threats for human health

– impact of socioeconomic and climate change on zooanthroponosis in the

Republic of Sakha (Yakutia), Russia. Int. J. Circumpolar Health, 79(1), doi:10

.1080/22423982.2020.1715698.

Huber, V., etal., 2020b: Temperature-related excess mortality in German cities

at 2degrees C and higher degrees of global warming. Environ. Res., 186,

doi:10.1016/j.envres.2020.109447.

Huda, M.S., 2021: An ecological response to ethno-nationalistic populism:

grassroots environmental peacebuilding in south Asia. Int. Aff., 97(1), 119–

138.

Huda, M.S. and S.H. Ali, 2018: Environmental peacebuilding in South Asia:

establishing consensus on hydroelectric projects in the Ganges-Brahmaputra-

Meghna (GBM) Basin. Geoforum, 96, 160–171.

Hueffer, K., M. Ehrlander, K. Etz and A. Reynolds, 2019: One health in the

circumpolar North. Int. J. Circumpolar Health, 78(1), doi:10.1080/2242398

2.2019.1607502.

Hundessa, S., etal., 2018: Projecting potential spatial and temporal changes

in the distribution of Plasmodium vivax and Plasmodium falciparum

malaria in China with climate change. Sci. Total Environ., 627, 1285–1293,

doi:10.1016/j.scitotenv.2018.01.300.

Hunter, L.M. and D.H. Simon, 2017: Might climate change the “healthy migrant”

effect? Glob. Environ. Change, 47, 133–142.

Hunter, L.M., et al., 2021: Scales and sensitivities in climate vulnerability,

displacement, and health. Popul. Environ., 43(1), 61–81, doi:10.1007/

s11111-021-00377-7.

Hussain-Alkhateeb, L., et al., 2018: Early warning and response system

(EWARS) for dengue outbreaks: recent advancements towards widespread

applications in critical settings. PLoS ONE, 13(5), doi:10.1371/journal.

pone.0196811.

Hynes, W., T. Benjamin, L. Patrick and L. Igor, 2020: Bouncing forward: a

resilience approach to dealing with COVID-19 and future systemic shocks.

Environ. Syst. Decis., 40, 174–184.

Ide, T., 2019: The impact of environmental cooperation on peacemaking:

deﬁnitions, mechanisms, and empirical evidence. Int. Stud. Rev., 21(3),

327–346.

Ide, T., 2020: The dark side of environmental peacebuilding. World Dev., 127,

104777.

Ide, T., etal., 2021: The past and future (s) of environmental peacebuilding. Int.

Aff., 97(1), 1–16.

Ide, T., M. Brzoska, J. Donges and C. Schleussner, 2020: Multi-method

evidence for when and how climate-related disasters contribute to armed

conﬂict risk. Glob. Environ. Change Policy Dimens., 62, doi:10.1016/j.

gloenvcha.2020.102063.

Ide, T. and C. Frohlich, 2015: Socio-environmental cooperation and conﬂict?

A discursive understanding and its application to the case of Israel and

Palestine. Earth Syst. Dyn., 6(2), 659–671, doi:10.5194/esd-6-659-2015.

Ide, T. and A. Tubi, 2020: Education and environmental peacebuilding: insights

from three projects in Israel and Palestine. Ann. Am. Assoc. Geogr., 110(1),

1–17.

Ikaheimo, T.M., 2018: Cardiovascular diseases, cold exposure and exercise.

Temperature, 5(2), 123–146, doi:10.1080/23328940.2017.1414014.

Im, E., J. Pal and E. Eltahir, 2017: Deadly heat waves projected in the densely

populated agricultural regions of South Asia. Sci. Adv., 3(8), doi:10.1126/

sciadv.1603322.

IMF, 2020: World Economic Outlook: a Long and Difﬁcult Ascent. International

Monetary Fund, Washington, DC.

Internal Displacement Monitoring Centre, 2020: Global Report on Internal

Displacement. IDMC, Geneva. 136p.

International Organization for Migration, 2019: World Migration Report 2020.

Geneva, https://www.un.org/sites/un2.un.org/ﬁles/wmr_2020.pdf. Last

accessed 3 September 2021.

IPCC, 2019a: IPCC Special Report on the Ocean and Cryosphere in a Changing

Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor,

E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold,

B. Rama, N.M. Weyer (eds.)]. https://report.ipcc.ch/srocc/pdf/SROCC_

FinalDraft_FullReport.pdf . (1170 pp).

IPCC, 2019b: Climate Change and Land: an IPCC special report on climate

change, desertiﬁcation, land degradation, sustainable land management,

food security, and greenhouse gas ﬂuxes in terrestrial ecosystems [P.R.

Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C.

Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S.

Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K.

Kissick, M. Belkacemi, J. Malley, (eds.)].

IPES-Food, 2018: Breaking Away from Industrial Food and Farming Systems:

Seven Case Studies of Agroecological Transition. IPES Secretariat, Brussels.

IPES-Food, 2020: The Added Value(s) of Agroecology: Unlocking the Potential

for Transition in West Africa. IPES Secretariat, Brussels.

Ishida, H., etal., 2014: Global-scale projection and its sensitivity analysis of

the health burden attributable to childhood undernutrition under the latest

scenario framework for climate change research. Environ. Res. Lett., 9(6),

doi:10.1088/1748-9326/9/6/064014.

Islam, M., M. Alharthi and M. Alam, 2019: The impacts of climate change on

road trafﬁc accidents in Saudi Arabia. Climate, 7(9), 103.

Islam, M.R. and M. Hasan, 2016: Climate-induced human displacement: a case

study of Cyclone Aila in the south-west coastal region of Bangladesh. Nat.

Hazards, 81(2), 1051–1071.

Islam, M.R. and M. Shamsuddoha, 2017: Socioeconomic consequences of

climate induced human displacement and migration in Bangladesh. Int.

Sociol., 32(3), 277-298.

Islam, M.Z., O. Kolade and G. Kibreab, 2018: Post-disaster housing

reconstruction: the impact of resourcing in post-cyclones Sidr and Aila in

Bangladesh. J. Int. Dev., 30(6), 934–960.

Islam, S., C. Chu, J. Smart and L. Liew, 2020: Integrating disaster risk reduction

and climate change adaptation: a systematic literature review. Clim. Dev.,

12(3), 255–267, doi:10.1080/17565529.2019.1613217.

Ito, Y., M. Akahane and T. Imamura, 2018: Impact of temperature in summer on

emergency transportation for heat-related diseases in Japan. Chin. Med. J.,

131, 574–582, doi:10.4103/0366-6999.226061.

Jaakkola, J.J.K., S. Juntunen and K. Nakkalajarvi, 2018: The holistic effects of

climate change on the culture, well-being, and health of the Saami, the only

indigenous people in the European Union. Curr. Environ. Health Rep., 5(4),

401–417, doi:10.1007/s40572-018-0211-2.

Jacob, B. and M. Kazaura, 2021: Access to safe water, sanitation, and hygiene: a

cross-sectional study among the Maasai in Tanzania. Am. J. Trop. Med. Hyg.,

doi:10.4269/ajtmh.20-0134.

Jahn, S. and E. Hertig, 2021: Modeling and projecting health-relevant combined

ozone and temperature events in present and future Central European

climate. Air Qual. Atmos. Health, 14(4), 563–580, doi:10.1007/s11869-020-

00961-0.

Jalilov, S.M., S.A. Amer and F.A. Ward, 2018: Managing the water-energy-food

nexus: opportunities in Central Asia. J. Hydrol., 557, 407–425.

Jamero, M., et al., 2017: Small-island communities in the Philippines prefer

local measures to relocation in response to sea-level rise. Nat. Clim. Change,

7(8), 581–586, doi:10.1038/nclimate3344.

James, L.E., C. Welton-Mitchell, J.R. Noel and A.S. James, 2020: Integrating

mental health and disaster preparedness in intervention: a randomized

controlled trial with earthquake and ﬂood-affected communities in Haiti.

Psychol. Med., 50(2), 342–352, doi:10.1017/s0033291719000163.

James, S.L., etal., 2018: Global, regional, and national incidence, prevalence,

and years lived with disability for 354 diseases and injuries for 195countries

1147

Health, Wellbeing and the Changing Structure of Communities Chapter 7

and territories, 1990–2017: a systematic analysis for the Global Burden of

Disease Study 2017. Lancet, 392(10159), 1789–1858.

Jang, J., etal., 2020: Roles of urban heat anomaly and land-use/land-cover

on the heat-related mortality in the national capital region of South

Korea: a multi-districts time-series study. Environ. Int., 145, doi:10.1016/j.

envint.2020.106127.

Jardine, S., M. Fisher, S. Moore and J. Samhouri, 2020: Inequality in the economic

impacts from climate shocks in ﬁsheries: the case of harmful algal blooms.

Ecol. Econ., 176, doi:10.1016/j.ecolecon.2020.106691.

Jay, O., et al., 2021: Reducing the health effects of hot weather and heat

extremes: from personal cooling strategies to green cities. Lancet,

398(10301), 16, doi:10.1016/S0140-6736(21)01209-5.

Jehn, M., etal., 2013: Tele-monitoring reduces exacerbation of COPD in the

context of climate change – a randomized controlled trial. Environ. Health,

12, 99, doi:10.1186/1476-069x-12-99.

Jennings, V. and O. Bamkole, 2019: The relationship between social cohesion

and urban green space: an avenue for health promotion. Int. J. Environ. Res.

Public Health, 16(3), doi:10.3390/ijerph16030452.

Jennings, V., C.E. Reid and C.H. Fuller, 2021: Green infrastructure can limit but

not solve air pollution injustice. Nat. Commun., 12(1), doi:10.1038/s41467-

021-24892-1.

Jessoe, K., D. T. Manning, and J. E. Taylor, 2018: Climate change and labour

allocation in rural Mexico: evidence from annual ﬂuctuations in weather.

EconJ, 128(608), 230–261.

Jim, C.Y., C.K. van den Bosch and W.Y. Chen, 2018: Acute challenges and

solutions for urban forestry in compact and densifying cities. J. Urban Plan.

Dev., 144(3), doi:10.1061/(asce)up.1943-5444.0000466.

Johansson, J.M., etal., 2020: The European heat wave of 2018 and its promotion

of the ozone climate penalty in southwest Sweden. Boreal Environ. Res., 25,

39–50.

Johnson-Jennings, M., et al., 2020: Ode’imin Giizis: proposing and piloting

gardening as an indigenous childhood health intervention. J. Health Care

Poor Underserved, 31(2), 871–888, doi:10.1353/hpu.2020.0066.

Johnson, C., et al., 2013: Social protection and climate change: emerging

issues for research, policy and practice. Dev. Policy Rev., 31(s2), o2–o18,

doi:10.1111/dpr.12036.

Johnson, M.F., 2021: Local engagement in environmental peacebuilding:

protected area development as a pathway to peace in Afghanistan. Dev.

Pract., 1–13. DOI: 10.1080/09614524.2021.1937538

Johnston, F.H., A.M. Kavanagh, D.M. Bowman and R.K. Scott, 2002: Exposure

to bushﬁre smoke and asthma: an ecological study. Med. J. Aust., 176,

535–538.

Jones, A., 2017: Food insecurity and mental health status: a global analysis

of 149 countries. Am. J. Prev. Med., 53(2), 264–273, doi:10.1016/j.

amepre.2017.04.008.

Jones, B., etal., 2018: Avoiding population exposure to heat-related extremes:

demographic change vs climate change. Clim. Change, 146(3-4), 423–437,

doi:10.1007/s10584-017-2133-7.

Jones, L., 2019: Resilience isn’t the same for all: comparing subjective and

objective approaches to resilience measurement. Wiley Interdiscip. Rev.

Clim. Change, 10(1), doi:10.1002/wcc.552.

Jones, R., H. Bennett, G. Keating and A. Blaiklock, 2014: Climate change and

the right to health for Maori in Aotearoa/New Zealand. Health Hum. Rights,

16(1), 54–68.

Jones, R., A. Macmillan and P. Reid, 2020: Climate change mitigation policies

and co-impacts on indigenous health: a scoping review. Int. J. Environ. Res.

Public Health, 17(23), doi:10.3390/ijerph17239063.

Jost, C., et al., 2016: Understanding gender dimensions of agriculture and

climate change in smallholder farming communities. Clim. Dev., 8(2), 133–

144, doi:10.1080/17565529.2015.1050978.

Jugert, P., et al., 2016: Collective efﬁcacy increases pro-environmental

intentions through increasing self-efﬁcacy. J. Environ. Psychol., 48, 12–23,

doi:10.1016/j.jenvp.2016.08.003.

Jutla, A., R. Khan and R. Colwell, 2017: Natural disasters and cholera outbreaks:

current understanding and future outlook. Curr. Environ. Health Rep., 4(1),

99–107, doi:10.1007/s40572-017-0132-5.

Kabir, S.M.S., 2018: Psychological health challenges of the hill-tracts region for

climate change in Bangladesh. Asian J. Psychiatr., 34, 74–77, doi:10.1016/j.

ajp.2018.04.001.

Kaczan, D.J. and J. Orgill-Meyer, 2020: The impact of climate change on

migration: a synthesis of recent empirical insights. Clim. Change, 158(3),

281–300, doi:10.1007/s10584-019-02560-0.

Kadandale, S., et al., 2020: Primary health care and the climate crisis. Bull.

World Health Organ., 98(11), 818–820, doi:10.2471/blt.20.252882.

Kaffenberger, B.H., D. Shetlar, S.A. Norton and M. Rosenbach, 2017: The effect

of climate change on skin disease in North America. J. Am. Acad. Dermatol.,

76(1), 140–147, doi:10.1016/j.jaad.2016.08.014.

Kakinuma, K., et al., 2020: Flood-induced population displacements in the

world. Environ. Res. Lett., 15(12), 124029, doi:10.1088/1748-9326/abc586.

Kalilou, O., 2021: Climate change and conﬂict in the Sahel: the acacia gum tree

as a tool for environmental peacebuilding. Int. Aff., 97(1), 201–218.

Kälin, W., 2018: The global compact on migration: a ray of hope for disaster-

displaced persons. Int. J. Refug. Law, 30(4), 664–667, doi:10.1093/ijrl/

eey047.

Kalinda, C., M. Chimbari and S. Mukaratirwa, 2017: Effect of temperature

on the Bulinus globosus – Schistosoma haematobium system. Infect. Dis.

Poverty, 6, doi:10.1186/s40249-017-0260-z.

Kalisa, E., et al., 2018: Temperature and air pollution relationship during

heatwaves in Birmingham, UK. Sustain. Cities Soc., 43, 111–120,

doi:10.1016/j.scs.2018.08.033.

Kamal, A., etal., 2015: A recipe for change: reclamation of indigenous food

sovereignty in O-Pipon-na-Piwin Cree nation for decolonization, resource

sharing, and cultural restoration. Globalizations, 12(4), 559–575, doi:10.10

80/14747731.2015.1039761.

Kamal, M., et al., 2018: Mapping the global potential distributions of two

arboviral vectors Aedes aegypti and Ae. albopictus under changing climate.

PLoS ONE, 13(12), doi:10.1371/journal.pone.0210122.

Kampe, E.O.I., S. Kovats and S. Hajat, 2016: Impact of high ambient temperature

on unintentional injuries in high-income countries: a narrative systematic

literature review. BMJ Open, 6(2), doi:10.1136/bmjopen-2015-010399.

Keim, M., 2008: Building human resilience the role of public health preparedness

and response as an adaptation to climate change. Am. J. Prev. Med., 35(5),

508–516, doi:10.1016/j.amepre.2008.08.022.

Kelley, C.P., etal., 2015: Climate change in the Fertile Crescent and implications

of the recent Syrian drought. Proc. Natl. Acad. Sci. U. S. A., 112(11), 3241–

3246, doi:10.1073/pnas.1421533112.

Kellog, M., 2020: Women Building Resilient Cities in the Context of Climate

Change Lessons from Freetown, Sierra Leone. Institute for Women, Peace

and Security, Georgetown.

Kelly, F.J., I.S. Mudway and J.C. Fussell, 2021: Air pollution and asthma: critical

targets for effective action. Pulm. Ther., 7(1), 9–24, doi:10.1007/s41030-020-

00138-1.

Kelman, I., J. Field, K. Suri and G.M. Bhat, 2018: Disaster diplomacy in Jammu

and Kashmir. Int. J. Disaster Risk Reduct., 31, 1132–1140.

Kelman, I., et al., 2019: Does climate change inﬂuence people’s migration

decisions in Maldives? Clim. Change, 153(1-2), 285–299, doi:10.1007/

s10584-019-02376-y.

Kendrovski, V., etal., 2017: Quantifying projected heat mortality impacts under

21st-century warming conditions for selected European countries. Int. J.

Environ. Res. Public Health, 14(7), doi:10.3390/ijerph14070729.

Kennedy, J. and L. King, 2014: The political economy of farmers’ suicides in India:

indebted cash-crop farmers with marginal landholdings explain state-level

variation in suicide rates. Glob. Health, 10(1), 16–16, doi:10.1186/1744-

8603-10-16.

1148

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Kennedy, S., M.P. Kidd, J.T. McDonald and N. Biddle, 2015: The healthy immigrant

effect: patterns and evidence from four countries. Int. Migr. Integr., 16(2),

317–332, doi:10.1007/s12134-014-0340-x.

Kenney, M. A., A.C. Janetos and G.C. Lough, 2016: Building an integrated U.S.

National Climate Indicators System. In: The US National Climate Assessment:

Innovations in Science and Engagement [Jacobs, K., S. Moser and J.

Buizer(eds.)]. Springer International Publishing, Cham, pp. 85–96. ISBN 978-

3319418025.

Kerr, Z.Y., etal., 2020: The epidemiology and management of exertional heat

illnesses in high school sports during the 2012/2013–2016/2017 academic

years. J. Sport. Rehabil., 29(3), 332–338, doi:10.1123/jsr.2018-0364.

Kesetyaningsih, T.W., S. Andarini, Sudarto and H. Pramoedyo, 2018:

Determination of environmental factors affecting dengue incidence in

Sleman district, Yogyakarta, Indonesia. Afr. J. Infect. Dis., 12(1 Suppl), 13–25,

doi:10.2101/Ajid.12v1S.3.

Kessler, R.C., etal., 2008: Trends in mental illness and suicidality after Hurricane

Katrina. Molecular Psychiatry, 13, 374–384.

Keyes, K., etal., 2014: The burden of loss: unexpected death of a loved one

and psychiatric disorders across the life course in a national study. Am. J.

Psychiatry, 171(8), 864–871, doi:10.1176/appi.ajp.2014.13081132.

Khalil, I. A., et al., 2018: Morbidity, mortality, and long-term consequences

associated with diarrhoea from Cryptosporidium infection in children

younger than 5 years: a meta-analyses study. Lancet Glob. Health, 6(7),

e758–e768, doi:10.1016/s2214-109x(18)30283-3.

Khan, M.R., S. Huq, A.N. Risha, S.S. Alam, , 2021: High-density population and

displacement in Bangladesh. Science, 372(6548), 1290-1293. Available at:

http://science.sciencemag.org/content/372/6548/1290.abstract.

Khan, S., etal., 2015: Extreme weather events: Should drinking water quality

management systems adapt to changing risk proﬁles? Water Res., 85, 124–

136, doi:10.1016/j.watres.2015.08.018.

Khanian, M., B. Serpoush and N. Gheitarani, 2019: Balance between place

attachment and migration based on subjective adaptive capacity in response

to climate change: the case of Famenin County in Western Iran. Clim. Dev.,

11(1), 69–82, doi:10.1080/17565529.2017.1374238.

Khormi, H. and L. Kumar, 2016: Future malaria spatial pattern based on the

potential global warming impact in South and Southeast Asia. Geospat.

Health, 11(3), 290–298, doi:10.4081/gh.2016.416.

Kibret, S., etal., 2016: Malaria and large dams in sub-Saharan Africa: future

impacts in a changing climate. Malar. J., 15, doi:10.1186/s12936-016-1498-

Kim, H., etal., 2019a: Difference in temporal variation of temperature-related

mortality risk in seven major South Korean cities spanning 1998–2013. Sci.

Total Environ., 656, 986–996, doi:10.1016/j.scitotenv.2018.11.210.

Kim, J., etal., 2016: Effect of climate factors on the childhood pneumonia in

Papua New Guinea: a time-series analysis. Int. J. Environ. Res. Public Health,

13(2), doi:10.3390/ijerph13020213.

Kim, S., B. Fleisher and Y. Sun, 2017: The long-term health effects of fetal

malnutrition: evidence from the 1959–1961 China Great Leap Forward

famine. Health Econ., 26(10), 1264–1277.

Kim, Y., etal., 2019b: Suicide and ambient temperature: a multi-country multi-

city study. Environ. Health Perspect., 127(11), doi:10.1289/ehp4898.

King, A.D. and D.J. Karoly, 2017: Climate extremes in Europe at 1.5 and

2degrees of global warming. Environ. Res. Lett., 12(11), doi:10.1088/1748-

9326/aa8e2c.

King, U. and C. Furgal, 2014: Is hunting still healthy? Understanding the

interrelationships between indigenous participation in land-based practices

and human-environmental health. Int. J. Environ. Res. Public Health, 11(6),

5751–5782, doi:10.3390/ijerph110605751.

Kinney, P., 2018: Temporal trends in heat-related mortality: implications for

future projections. Atmosphere, 9, 409, doi:10.3390/atmos9100409.

Kipp, A., etal., 2019: The need for community-led, integrated and innovative

monitoring programmes when responding to the health impacts of climate

change. Int. J. Circumpolar Health, 78(2), doi:10.1080/22423982.2018.151

7581.

Kirtman, B., etal., 2014: Near-term climate change: projections and predictability.

In: Climate Change 2013: The Physical Science Basis, Cambridge University

Press, Cambridge. pp. 953–1028.

Kita, S.M., 2017: Urban vulnerability, disaster risk reduction and resettlement in

Mzuzu city, Malawi. Int. J. Disaster Risk Reduct., 22, 158–166.

Kjellstrom, T., et al., 2016: Heat, human performance, and occupational

health: a key issue for the assessment of global climate change impacts.

Annu. Rev. Public Health, 37(1), 97–112, doi:10.1146/annurev-

publhealth-032315-021740.

Kjellstrom, T., etal., 2018: Estimating population heat exposure and impacts

on working people in conjunction with climate change. Int. J. Biometeorol.,

62(3), 291–306, doi:10.1007/s00484-017-1407-0.

Kjellstrom, T., B. Lemke and J. Lee, 2019: Workplace heat: an increasing threat to

occupational health and productivity. Am. J. Ind. Med., 62(12), 1076–1078,

doi:10.1002/ajim.23051.

Klein, T., etal., 2019: A nation-wide analysis of tree mortality under climate

change: forest loss and its causes in Israel 1948–2017. For. Ecol. Manag.,

432, 840–849, doi:10.1016/j.foreco.2018.10.020.

Koc, C.B., P. Osmond and A. Peters, 2018: Evaluating the cooling effects of green

infrastructure: a systematic review of methods, indicators and data sources.

Sol. Energy, 166, 486–508, doi:10.1016/j.solener.2018.03.008.

Koren, O., B. Bagozzi and T. Benson, 2021: Food and water insecurity as causes

of social unrest: evidence from geolocated Twitter data. J. Peace Res., 58(1),

67–82, doi:10.1177/0022343320975091.

Koren, O. and B.E. Bagozzi, 2017: Living off the land: the connection between

cropland, food security, and violence against civilians. J. Peace Res., 54(3),

351–364, doi:10.1177/0022343316684543.

Koslov, L., A.Merdjanoff, E. Sulakshana and E.Klinenberg, 2021: When rebuilding

no longer means recovery: the stress of staying put after Hurricane Sandy.

Clim. Change, 165(3), 59, doi:10.1007/s10584-021-03069-1.

Kostyla, C., R. Bain, R. Cronk and J. Bartram, 2015: Seasonal variation of

fecal contamination in drinking water sources in developing countries:

a systematic review. Sci. Total Environ., 514, 333–343, doi:10.1016/j.

scitotenv.2015.01.018.

Koubi, V., 2019: Climate change and conﬂict. In: Annual Review of Political

Science [Levi, M. and N.L. Rosenblum(eds.)] Vol.22, Annual Reviews, Palo

Alto, pp. 343–360.

Koubi, V., T. Bˆhmelt, G. Spilker and L. Schaffer, 2018: The determinants of

environmental migrants’ conﬂict perception. Int. Organ., 72(4), 905–936.

Koubi, V., Q. Nguyen, G. Spilker and T. Bohmelt, 2021: Environmental

migrants and social-movement participation. J. Peace Res., 58(1), 18–32,

doi:10.1177/0022343320972153.

Koubi, V., Q.S. Nguyen and G.T. Böhmelt, 2020: Environmental migrants and

social-movement participation. J. Peace Res., 58(1), 18–32.

Koubi, V., G. Spilker, L. Schaffer and T. Bernauer, 2016a: Environmental

stressors and migration: evidence from Vietnam. World Dev., 79, 197–210,

doi:10.1016/j.worlddev.2015.11.016.

Koubi, V., G. Spilker, L. Schaffer and T. Böhmelt, 2016b: The role of environmental

perceptions in migration decision-making: evidence from both migrants and

non-migrants in ﬁve developing countries. Popul. Environ., 38(2), 134–163,

doi:10.1007/s11111-016-0258-7.

Kousky, C., 2016: Impacts of natural disasters on children. Future Child., 26(1),

73–92, doi:10.1353/foc.2016.0004.

Kovach, T. and K. Conca, 2016: Environmental priorities in post-conﬂict recovery:

efﬁcacy of the needs-assessment process. J. Peacebuild. Dev., 11(2), 4–24.

Kowalczewski, E. and J. Klein, 2018: Sámi youth health, the role of climate

change, and unique health-seeking behaviour. Int. J. Circumpolar Health,

77(1), 1454785, doi:10.1080/22423982.2018.1454785.

Kraemer, M., etal., 2019: Past and future spread of the arbovirus vectors Aedes

aegypti and Aedes albopictus. Nat. Microbiol., 4(5), 854–863, doi:10.1038/

s41564-019-0376-y.

1149

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Krakauer, N.Y., B.I. Cook and M.J. Puma, 2020: Effect of irrigation on humid heat

extremes. Environ. Res. Lett., 15(9), doi:10.1088/1748-9326/ab9ecf.

Krampe, F., 2017: Toward sustainable peace: a new research agenda for post-

conﬂict natural resource management. Glob. Environ. Polit., 17(4), 1–8.

Krampe, F. and S. Gignoux, 2018: Water service provision and peacebuilding

in East Timor: exploring the socioecological determinants for sustainings, V

peace. J. Interv. Statebuild., 12(2), 185–207.

Krampe, F., F. Hegazi and S.D. VanDeveer, 2021: Sustaining peace through

better resource governance: three potential mechanisms for environmental

peacebuilding. World Dev., 144, 105508.

Krause, J., W. Krause and P. Bränfors, 2018: Women’s participation in peace

negotiations and the durability of peace. Int. Interact., 44(6), 985–1016.

Kreppel, K.S., etal., 2014: A non-stationary relationship between global climate

phenomena and human plague incidence in Madagascar. PLoS Negl. Trop.

Dis., 8(10), doi:10.1371/journal.pntd.0003155.

Kron, A., 2019: Environmental Peacebuilding and the UN Security Council

[Schrijver, N. J. and N. M. Blokker (eds.)]. Nijhoff Law Specials, Netherlandol.

29. Brill, 248–266.

Krug, A., D. Fenner, A. Holtmann and D. Scherer, 2019: Occurrence and coupling

of heat and ozone events and their relation to mortality rates in Berlin,

Germany, between 2000 and 2014. Atmosphere, 10(6), doi:10.3390/

atmos10060348.

Krug, A., D. Fenner, H.G. Mucke and D. Scherer, 2020: The contribution of air

temperature and ozone to mortality rates during hot weather episodes in

eight German cities during the years 2000 and 2017. Nat. Hazards Earth

Syst. Sci., 20(11), 3083–3097, doi:10.5194/nhess-20-3083-2020.

Kubik, Z. and M. Maurel, 2016: Weather shocks, agricultural production and

migration: evidence from Tanzania. J. Dev. Stud., 52(5), 665–680, doi:10.108

0/00220388.2015.1107049.

Kuehn, L. and S. McCormick, 2017: Heat exposure and maternal health in the

face of climate change. Int. J. Environ. Res. Public Health, 14(8), doi:10.3390/

ijerph14080853.

Kuhnlein, H., B. Erasmus and D. Spigelski, 2013: Indigenous Peoples’ Food

Systems & Well-Being. Interventions & Policies for Healthy Communities.

Agriculture Organization United Nations, Rome.

Kumar, A., etal., 2018a: Mercury from wildﬁres: Global emission inventories

and sensitivity to 2000–2050 global change. Atmos. Environ., 173, 6–15,

doi:10.1016/j.atmosenv.2017.10.061.

Kumar, J.C., V. Gupta, U. Chattopadhyay and B.A. Sreeraman, 2018b: Migration

as adaptation strategy to cope with climate change: a study of farmers’

migration in rural India. Int. J. Clim. Change Strateg. Manag., 10(1), 121–

141, doi:10.1108/ijccsm-03-2017-0059.

Kumar, P., etal., 2019: The nexus between air pollution, green infrastructure

and human health. Environ. Int., 133, doi:10.1016/j.envint.2019.105181.

Kumar, S., R. Molitor and S. Vollmer, 2016: Drought and early child health in

rural India. Popul. Dev. Rev., 42(1), 53–68.

Kupferberg, J.S., 2021: Migration and dignity – relocation and adaptation in

the face of climate change displacement in the Paciﬁc – a human rights

perspective. Int. J. Hum. Rights, 1–26, doi:10.1080/13642987.2021.18895

15.

Kuusaana, E.D. and K.N. Bukari, 2015: Land conﬂicts between smallholders and

Fulani pastoralists in Ghana: evidence from the Asante Akim North District

(AAND). J. Rural Stud., 42, 52–62.

Lacey, G., etal., 2017: Transient climate and ambient health impacts due to

national solid fuel cookstove emissions. Proc. Natl. Acad. Sci. U.S.A., 114(6),

1269–1274, doi:10.1073/pnas.1612430114.

Lafond, K.E., etal., 2021: Global burden of inﬂuenza-associated lower respiratory

tract infections and hospitalizations among adults: a systematic review and

meta-analysis. PLoS Med., 18(3), doi:10.1371/journal.pmed.1003550.

Lai, D.Y., etal., 2019: A review of mitigating strategies to improve the thermal

environment and thermal comfort in urban outdoor spaces. Sci. Total

Environ., 661, 337–353, doi:10.1016/j.scitotenv.2019.01.062.

Lai, Y.H., 2018: The climatic factors affecting dengue fever outbreaks in

southern Taiwan: an application of symbolic data analysis. Biomed. Eng.

Online, 17(Suppl 2), 148, doi:10.1186/s12938-018-0575-4.

Lake, A.A., 2018: Neighbourhood food environments: food choice, foodscapes

and planning for health. Proc. Nutr. Soc., 77(3), 239–246, doi:10.1017/

s0029665118000022.

Lake, I.R., 2017: Food-borne disease and climate change in the United Kingdom.

Environ. Health, 16(Suppl 1), 117, doi:10.1186/s12940-017-0327-0.

Lake, I.R. and G.C. Barker, 2018: Climate change, foodborne pathogens and

illness in higher-income countries. Curr. Environ. Health Rep., 5(1), 187–196,

doi:10.1007/s40572-018-0189-9.

Lake, I.R., et al., 2019: Exploring campylobacter seasonality across Europe

using The European Surveillance System (TESSy)2008 to 2016. Euro Surveill.,

24(13), doi:10.2807/1560-7917.es.2019.24.13.180028.

Lake, I.R., etal., 2017: Climate change and future pollen allergy in Europe.

Environ. Health Perspect., 125(3), 385–391, doi:10.1289/ehp173.

Lake, L., et al., 2015: The effects of ﬂooding on dioxin and PCB levels in

food produced on industrial river catchments. Environ. Int., 77, 106–115,

doi:10.1016/j.envint.2015.01.006.

Lal, A., E. Fearnley and E. Wilford, 2019: Local weather, ﬂooding history and

childhood diarrhoea caused by the parasite Cryptosporidium spp.: a

systematic review and meta-analysis. Sci. Total Environ., 674, 300–306,

doi:10.1016/j.scitotenv.2019.02.365.

Lal, A., etal., 2016: Spatial and temporal variation in the association between

temperature and salmonellosis in NZ. Aust. N. Z. J. Public Health, 40(2),

165–169, doi:10.1111/1753-6405.12413.

Lam, E.K.S., et al., 2020: The impact of climate and antigenic evolution on

seasonal inﬂuenza virus epidemics in Australia. Nat. Commun., 11(1),

doi:10.1038/s41467-020-16545-6.

Lambert, H., etal., 2020: COVID-19 as a global challenge: towards an inclusive

and sustainable future. Lancet Planet. Health, 4(8), E312–E314, doi:10.1016/

s2542-5196(20)30168-6.

Laporta, G., etal., 2015: Malaria vectors in South America: current and future

scenarios. Parasites Vectors, 8, doi:10.1186/s13071-015-1038-4.

Lawson, E., R. Alare, A. Salifu and M. Thompson-Hall, 2019: Dealing with climate

change in semi-arid Ghana: understanding intersectional perceptions and

adaptation strategies of women farmers. GeoJournal, doi:10.1007/s10708-

019-09974-4.

Lazzaroni, S. and N. Wagner, 2016: Misfortunes never come singly: structural

change, multiple shocks and child malnutrition in rural Senegal. Econ. Hum.

Biol., 23, 246–262, doi:10.1016/j.ehb.2016.10.006.

Le Masson, V., C. Benoudji and S. Reyes, 2019: Everyday Violence and its Impact

on Insecurity and Resilience in Chad. UNEP, Nairobi.

Le Masson, V., etal., 2016: Gender Resilience from Theory to Practice. Building

Resilience and Adaptation to Climate Extremes and Disasters. Overseas

Development Institute, London, 60 pp.

Le, P., et al., 2019: Predicting the direct and indirect impacts of climate

change on malaria in coastal Kenya. PLoS ONE, 14(2), doi:10.1371/journal.

pone.0211258.

Leard, B. and K. Roth, 2019: Voluntary exposure beneﬁts and the costs of climate

change. J. Assoc. Environ. Resour. Econ., 6(1), 151–185, doi:10.1086/700268.

Lee, E.C., etal., 2019: The projected impact of geographic targeting of oral

cholera vaccination in sub-Saharan Africa: a modeling study. PLoS Med.,

16(12), e1003003, doi:10.1371/journal.pmed.1003003.

Lee, J., S. Lee and X. Jiang, 2017a: Cyanobacterial toxins in freshwater and food:

important sources of exposure to humans. Annu. Rev. Food Sci. Technol., 8,

281–304, doi:10.1146/annurev-food-030216-030116.

Lee, J.S., et al., 2017b: Early warning signal for dengue outbreaks and

identiﬁcation of high risk areas for dengue fever in Colombia using climate

and non-climate datasets. BMC Infect. Dis., 17(1), 480, doi:10.1186/s12879-

017-2577-4.

1150

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Lee, J.Y. and H. Kim, 2016: Projection of future temperature-related mortality

due to climate and demographic changes. Environ. Int., 94, 489–494,

doi:10.1016/j.envint.2016.06.007.

Lee, S., etal., 2018a: Mental disease-related emergency admissions attributable

to hot temperatures. Sci. Total Environ., 616, 688–694.

Lee, S.H., et al., 2021: Trends in recent waterborne and foodborne disease

outbreaks in South Korea, 2015–2019. Osong Public Health Res. Perspect.,

12(2), 73–79, doi:10.24171/j.phrp.2021.12.2.04.

Lee, W., et al., 2018b: Temporal changes in mortality attributed to heat

extremes for 57 cities in Northeast Asia. Sci. Total Environ., 616-617, 703–

709, doi:10.1016/j.scitotenv.2017.10.258.

Lee, W., et al., 2020: Projections of excess mortality related to diurnal

temperature range under climate change scenarios: a multi-country

modelling study. Lancet Planet. Health, 4(11), E512–E521.

Leedale, J., et al., 2016: Projecting malaria hazard from climate change in

eastern Africa using large ensembles to estimate uncertainty. Geospat.

Health, 11(1 Suppl), 393, doi:10.4081/gh.2016.393.

Lei, X.N., et al., 2021: Temperature changes between neighboring days and

childhood asthma: a seasonal analysis in Shanghai, China. Int. J. Biometeorol.,

65(6), 827–836, doi:10.1007/s00484-020-02057-x.

Leichenko, R. and J.A. Silva, 2014: Climate change and poverty: vulnerability,

impacts, and alleviation strategies. Wiley Interdiscip. Rev. Change, 5(4),

539–556, doi:10.1002/wcc.287.

Leiserowitz, A., etal., 2017: Climate Change in the American Mind: October

2017. Yale Program on Climate Change Communication. Yale University and

George Mason University, New Haven, CT.

Lemelin, H., et al., 2010: Climate change, wellbeing and resilience in the

Weenusk First Nation at Peawanuck: the Moccasin Telegraph goes global.

RRH, 10(2), 106-123.

León, V.M., R. Moreno-González, V. García and J.A. Campillo, 2017: Impact

of ﬂash ﬂood events on the distribution of organic pollutants in surface

sediments from a Mediterranean coastal lagoon (Mar Menor, SE Spain).

Environ. Sci. Pollut. Res. Int., 24(5), 4284–4300, doi:10.1007/s11356-015-

4628-y.

Lerner, H. and C. Berg, 2015: The concept of health in One Health and some

practical implications for research and education: what is One Health?

Infect. Ecol. Epidemiol., 5, 25300, doi:10.3402/iee.v5.25300.

Levi, M., T. Kjellstrom and A. Baldasseroni, 2018: Impact of climate change on

occupational health and productivity: a systematic literature review focusing

on workplace heat. Med. Lav., 109, doi:10.23749/mdl.v109i3.6851.

Levy, A., 2016: Climate change, urban heat islands and health effects: urban

planning in Paris. Environ. Risques Sante, 15(4), 351–356, doi:10.1684/

ers.2016.0888.

Levy, B.S. and J.A. Patz, 2015: Climate change, human rights, and social justice.

Ann. Glob. Health, 81(3), 310–322, doi:10.1016/j.aogh.2015.08.008.

Levy, K., S.M. Smith and E.J. Carlton, 2018: Climate change impacts on

waterborne diseases: moving toward designing interventions. Curr. Environ.

Health Rep., 5(2), 272–282, doi:10.1007/s40572-018-0199-7.

Levy, K., A.P. Woster, R.S. Goldstein and E.J. Carlton, 2016: Untangling the

impacts of climate change on waterborne diseases: a systematic review of

relationships between diarrheal diseases and temperature, rainfall, ﬂooding,

and drought. Environ. Sci. Technol., 50(10), 4905–4922, doi:10.1021/acs.

est.5b06186.

Lewis, D., L. Williams and R. Jones, 2020: A radical revision of the public health

response to environmental crisis in a warming world: contributions of

Indigenous knowledges and Indigenous feminist perspectives. Can. J. Public

Health, 111(6), 897–900, doi:10.17269/s41997-020-00388-1.

Leyk, S., et al., 2017: Internal and international mobility as adaptation to

climatic variability in contemporary Mexico: evidence from the integration

of census and satellite data. Popul. Space Place, doi:10.1002/psp.2047.

Li, C. and Z. Zhu, 2018: Research and application of a novel hybrid air quality

early-warning system: a case study in China. Sci. Total Environ., 626, 1421–

1438, doi:10.1016/j.scitotenv.2018.01.195.

Li, M., S. Ferreira and T. Smith, 2020: Temperature and self-reported mental

health in the United States. PLoS ONE, 15(3), doi:10.1371/journal.

pone.0230316.

Li, T., et al., 2015: Heat-related mortality projections for cardiovascular and

respiratory disease under the changing climate in Beijing, China. Sci.

Rep., 5, 11441, doi:10.1038/srep11441. https://www.nature.com/articles/

srep11441#supplementary-information.

Li, X.Q., L.C. Stringer and M. Dallimer, 2021: The spatial and temporal

characteristics of urban heat island intensity: implications for East Africa’s

urban development. Climate, 9(4), doi:10.3390/cli9040051.

Li, Y., etal., 2014: Extremely cold and hot temperatures increase the risk of

diabetes mortality in metropolitan areas of two Chinese cities. Environ. Res.,

134, 91–97, doi:10.1016/j.envres.2014.06.022.

Li, Y., etal., 2018: Projecting future climate change impacts on heat-related

mortality in large urban areas in China. Environ. Res., 163, 171–185,

doi:10.1016/j.envres.2018.01.047.

Liang, L. and P. Gong, 2017: Climate change and human infectious diseases: a

synthesis of research ﬁndings from global and spatio-temporal perspectives.

Environ. Int., 103, 99–108.

Liang, W., etal., 2018: Mapping the epidemic changes and risks of hemorrhagic

fever with renal syndrome in Shaanxi Province, China, 2005–2016. Sci. Rep.,

8(1), 749, doi:10.1038/s41598-017-18819-4.

Ligon, G. and J. Bartram, 2016: Literature review of associations among

attributes of reported drinking water disease outbreaks. Int. J. Environ. Res.

Public Health, 13(6), doi:10.3390/ijerph13060527.

Lim, T.K. and W.T. Siow, 2018: Pneumonia in the tropics. Respirology, 23(1),

28–35, doi:10.1111/resp.13137.

Lim, Y., etal., 2015: Effects of cold and hot temperature on dehydration: a

mechanism of cardiovascular burden. Int. J. Biometeorol., 59(8), 1035–1043,

doi:10.1007/s00484-014-0917-2.

Limaye, V.S., etal., 2018: Climate change and heat-related excess mortality

in the eastern USA. EcoHealth, 15(3), 485–496, doi:10.1007/s10393-018-

1363-0.

Lin, H.L., etal., 2013: Temperature changes between neighboring days and

mortality in summer: a distributed lag non-linear time series analysis. Plos

One, 8(6), doi:10.1371/journal.pone.0066403.

Lin, S., M. Sun, E. Fitzgerald and S.A. Hwang, 2016: Did summer weather factors

affect gastrointestinal infection hospitalizations in New York State? Sci. Total

Environ., 550, 38–44.

Linke, A.M., etal., 2018: The consequences of relocating in response to drought:

human mobility and conﬂict in contemporary Kenya. Environ. Res. Lett.,

13(9), 94014.

Liu-Helmersson, J., etal., 2019: Estimating past, present, and future trends in

the global distribution and abundance of the arbovirus vector Aedes aegypti

under climate change scenarios. Front. Public Health, 7, doi:10.3389/

fpubh.2019.00148.

Liu-Helmersson, J., etal., 2016: Climate change and Aedes vectors: 21st century

projections for dengue transmission in Europe. EBioMedicine, 7, 267–277,

doi:10.1016/j.ebiom.2016.03.046.

Liu, C., Z. Yavar and Q. Sun, 2015a: Cardiovascular response to thermoregulatory

challenges. Am. J. Physiol. Circ. Physiol., 309(11), H1793–H1812, doi:10.1152/

ajpheart.00199.2015.

Liu, K., etal., 2015b: A national assessment of the epidemiology of severe fever

with thrombocytopenia syndrome, China. Sci. Rep., 5, 9679, doi:10.1038/

srep09679.

Liu, Q., et al., 2020a: Changing rapid weather variability increases

inﬂuenza epidemic risk in a warming climate. Environ. Res. Lett., 15(4),

doi:10.1088/1748-9326/ab70bc.

Liu, X., 2020: Reductions in labor capacity from intensiﬁed heat stress in

China under future climate change. Int. J. Environ. Res. Public Health, 17(4),

doi:10.3390/ijerph17041278.

1151

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Liu, Y., et al., 2014: Temporal relationship between hospital admissions for

pneumonia and weather conditions in Shanghai, China: a time-series

analysis. BMJ Open, 4(7), doi:10.1136/bmjopen-2014-004961.

Liu, Y., et al., 2020b: Natural outdoor environment, neighbourhood social

cohesion and mental health: using multilevel structural equation modelling,

streetscape and remote-sensing metrics. Urban For. Urban Green., 48,

doi:10.1016/j.ufug.2019.126576.

Liu, Z., etal., 2017: Global and regional changes in exposure to extreme heat

and the relative contributions of climate and population change. Sci. Rep., 7,

doi:10.1038/srep43909.

Liu, Z., etal., 2018: Association between ﬂoods and infectious diarrhea and

their effect modiﬁers in Hunan province, China: a two-stage model. Sci. Total

Environ., 626, 630–637, doi:10.1016/j.scitotenv.2018.01.130.

Liverpool-Tasie, L.S.O., T. Reardon and B. Belton, 2021: “Essential non-

essentials”: COVID-19 policy missteps in Nigeria rooted in persistent myths

about African food supply chains. Appl. Econ. Perspect. Policy, 43(1), 205–

224, doi:10.1002/aepp.13139.

Lloyd, 2018: A global-level model of the potential impacts of climate change on

child stunting via income and food price in 2030. Environ. Health Perspect.,

126(9), 97007, doi:10.1289/ehp2916.

Lo Iacono, G., etal., 2017: Challenges in developing methods for quantifying the

effects of weather and climate on water-associated diseases: a systematic

review. PLoS Negl. Trop. Dis., 11(6), doi:10.1371/journal.pntd.0005659.

Loebach, P., 2016: Household migration as a livelihood adaptation in response

to a natural disaster: Nicaragua and Hurricane Mitch. Popul. Environ, 38(2),

185-206

Lopez-i-Gelats, F., et al., 2015: Adaptation strategies of Andean pastoralist

households to both climate and non-climate changes. Hum. Ecol., 43(2),

267–282, doi:10.1007/s10745-015-9731-7.

Lopez, M.S., G.V. Muller and W.F. Sione, 2018: Analysis of the spatial distribution

of scientiﬁc publications regarding vector-borne diseases related to climate

variability in South America. Spat. Spatiotemp. Epidemiol., 26, 35–93,

doi:10.1016/j.sste.2018.04.003.

Lovell, R., M. Depledge and S. Maxwell, 2018: Health and the Natural

Environment: a Review of Evidence, Policy, Practice and Opportunities for

the Future. Department for Environment, Food and Rural Affairs (DFRA),

London.

Lu, J.G., 2020: Air pollution: a systematic review of its psychological,

economic, and social effects. Curr. Opin. Psychol., 32, 52–65, doi:10.1016/j.

copsyc.2019.06.024.

Lu, P., et al., 2021: Temporal trends of the association between ambient

temperature and cardiovascular mortality: a 17-year case-crossover study.

Environ. Res. Lett., 16(4), doi:10.1088/1748-9326/abab33.

Lu, X., etal., 2016: Detecting climate adaptation with mobile network data

in Bangladesh: anomalies in communication, mobility and consumption

patterns during Cyclone Mahasen. Clim. Change, 138(3-4), 505–519.

Luechinger, S., 2010: Life satisfaction and transboundary air pollution. Econ.

Lett., 107(1), 4–6, doi:10.1016/j.econlet.2009.07.007.

Lund, C., E. Baron and E. Breuer, 2018: Social determinants of mental disorders

and the Sustainable Development Goals: a systematic review of reviews.

Lancet Psychiatry, 5,357-369.

Lundgren, A.D., 2018: Climate change and skin disease. Cutis, 101(4), E12–E14.

Lynch, M.J., P.B. Stretesky, M. A. Long and K.L. Barrett, 2020: The climate

change-temperature-crime hypothesis: evidence from a sample of 15 large

US cities, 2002 to 2015. Int. J. Offender Ther. Comp. Criminol.DOI: https://doi.

org/10.1177/0306624X20969934

M’Bra, R.K., etal., 2018: Impact of climate variability on the transmission risk

of malaria in northern Cote d’Ivoire. PLoS ONE, 13(6), e182304, doi:10.1371/

journal.pone.0182304.

Ma, F. and X. Yuan, 2021: Impact of climate and population changes on the

increasing exposure to summertime compound hot extremes. Sci. Total.

Environ., 772, doi:10.1016/j.scitotenv.2021.145004.

Ma, R., etal., 2019: Estimation of work-related injury and economic burden

attributable to heat stress in Guangzhou, China. Sci. Total Environ., 666,

147–154, doi:10.1016/j.scitotenv.2019.02.201.

Macdiarmid, J. and S. Whybrow, 2019: Nutrition from a climate

change perspective. Proc. Nutr. Soc., 78(3), 380–387, doi:10.1017/

s0029665118002896.

MacFadden, D., et al., 2018: Antibiotic resistance increases with local

temperature. Nat. Clim. Change, 8(6), 510, doi:10.1038/s41558-018-0161-6.

Mach, K., et al., 2019: Climate as a risk factor for armed conﬂict. Nature,

571(7764), 193, doi:10.1038/s41586-019-1300-6.

Mach, K.J., etal., 2020: Directions for research on climate and conﬂict. Earth’s

Future. DOI: https://doi.org/10.1029/2020EF001532

Machalaba, C., et al., 2015: Climate change and health: transcending silos

to ﬁnd solutions. Ann. Glob. Health., 81(3), 445–458, doi:10.1016/j.

aogh.2015.08.002.

Macintyre, H.L. and C. Heaviside, 2019: Potential beneﬁts of cool roofs in

reducing heat-related mortality during heatwaves in a European city.

Environ. Int., 127, 430–441, doi:10.1016/j.envint.2019.02.065.

Macintyre, H.L., C. Heaviside, X. Cai and R. Phalkey, 2021: The winter urban

heat island: Impacts on cold-related mortality in a highly urbanized

European region for present and future climate. Environ. Int., 154, 106530,

doi:10.1016/j.envint.2021.106530.

Macintyre, H.L., et al., 2018: Assessing urban population vulnerability and

environmental risks across an urban area during heatwaves – implications

for health protection. Sci. Total Environ., 610, 678–690, doi:10.1016/j.

scitotenv.2017.08.062.

Mackenzie, J. and M. Jeggo, 2019: The one health approach – why is it so

important? Trop. Med. Infect. Dis., 4(2), 88, doi:10.3390/tropicalmed4020088.

Maclean, C., I. Popovici and T. French, 2016: Are natural disasters in early

childhood associated with mental health and substance use disorders as an

adult? Soc. Sci. Med., 151, 78–91.

Macnaughton, P., etal., 2018: Energy savings, emission reductions, and health

co-beneﬁts of the green building movement. J. Expo. Sci. Environ. Epidemiol.,

28(4), 307–318, doi:10.1038/s41370-017-0014-9.

Macnight Ngwese, N., etal., 2018: Traditional and local knowledge practices

for disaster risk reduction in northern Ghana. Sustainability, 10(3), 825.

Madureira, H., et al., 2021: Evidences on adaptive mechanisms for

cardiorespiratory diseases regarding extreme temperatures and air

pollution: a comparative systematic review. Geogr. Sustain., doi:10.1016/j.

geosus.2021.08.001.

Magnan, A., et al., 2016: Addressing the risk of maladaptation to climate

change. Wiley Interdiscip. Rev. Clim. Change, 7, 646–665, doi:10.1002/

wcc.409.

Mahajan, P.Y. and Dean, 2020: Taken by storm: hurricanes, migrant networks

and US immigration. Am. Econ. J. Appl. Econ., 12(2), 250–277, doi:10.1257/

app.20180438.

Maharjan, A., etal., 2020: Migration and household adaptation in climate-

sensitive hotspots in South Asia. Curr. Clim. Change Rep., doi:10.1007/

s40641-020-00153-z.

Maini, R., C. Lorcan, B. Kevin and M. Virginia, 2017: The Sendai framework for

disaster risk reduction and its indicators—where does health ﬁt in? Int. J.

Disaster Risk Sci., 8(2), 150–155, doi:10.1007/s13753-017-0120-2.

Maizlish, N., N.J. Linesch and J. Woodcock, 2017: Health and greenhouse gas

mitigation beneﬁts of ambitious expansion of cycling, walking, and transit

in California. J. Transp. Health, 6, 490–500, doi:10.1016/j.jth.2017.04.011.

Mala, S. and M.K. Jat, 2019: Implications of meteorological and physiographical

parameters on dengue fever occurrences in Delhi. Sci. Total Environ., 650(Pt

2), 2267–2283, doi:10.1016/j.scitotenv.2018.09.357.

Maldonado, J., et al., 2021: Addressing the challenges of climate-driven

community-led resettlement and site expansion: knowledge sharing,

storytelling, healing, and collaborative coalition building. J. Environ. Stud.

Sci., 1–11, doi:10.1007/s13412-021-00695-0.

1152

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Mall, R.K., et al., 2019: Disaster risk reduction including climate change

adaptation over South Asia: challenges and ways forward. Int. J. Disaster

Risk Sci., 10(1), 14–27, doi:10.1007/s13753-018-0210-9.

Mallick, B., 2019: The nexus between socio-ecological system, livelihood

resilience, and migration decisions: empirical evidence from Bangladesh.

Sustainability, 11(12), doi:10.3390/su11123332.

Mallick, B., B. Ahmed and J. Vogt, 2017: Living with the risks of cyclone disasters

in the South-Western coastal region of Bangladesh. Environments, 4(1), 13.

Mallick, B. and J. Schanze, 2020: Trapped or voluntary? Non-migration despite

climate risks. Sustainability, 12(11), doi:10.3390/su12114718.

Mamo, D. (ed.), 2020: The Indigenous World 2020. The International Work

Group for Indigenous Affairs (IWGIA), Eks-Skolen Trykkeri, Copenhagen,

Denmark, 784 pp.

Manning, C. and S. Clayton, 2018: Threats to mental health and wellbeing

associated with climate change, In Psychology and climate change.

Academic Press, London, UK, pp.217–244.

Maor, M., 2018: Rhetoric and doctrines of policy over- and underreactions

in times of crisis. Policy Polit., 46(1), 47–63, doi:10.1332/03055731

7x14843233064353.

Mapou, A.E.M., etal., 2017: Environmental factors and ﬂuctuations in daily

crime rates. J. Environ. Health, 80(5), 8–22.

Marcus, H. and L. Hanna, 2020: Understanding national barriers to climate

change adaptation for public health: a mixed-methods survey of national

public health representatives. Int. J. Health Gov., doi:10.1108/ijhg-06-2020-

0061.

Mares, D.M. and K.W. Moffett, 2016: Climate change and interpersonal

violence: a “global” estimate and regional inequities. Clim. Change, 135(2),

297–310.

Mares, D.M. and K.W. Moffett, 2019: Climate change and crime revisited: an

exploration of monthly temperature anomalies and UCR crime data. Environ.

Behav., 51(5), 502–529.

Marinaccio, A., etal., 2019: Nationwide epidemiological study for estimating

the effect of extreme outdoor temperature on occupational injuries in Italy.

Environ. Int., 133, 105176.

Maringe, C., et al., 2020: The impact of the COVID-19 pandemic on cancer

deaths due to delays in diagnosis in England, UK: a national, population-

based, modelling study. Lancet Oncol., 21(8), 1023–1034, doi:10.1016/

s1470-2045(20)30388-0.

Marini, G., etal., 2020: A quantitative comparison of West Nile virus incidence

from 2013 to 2018 in Emilia-Romagna, Italy. PLoS Negl. Trop. Dis., 14(1).

DOI: https://doi.org/10.1371/journal.pntd.0007953

Marino, E., 2018: Adaptation privilege and voluntary buyouts: perspectives

on ethnocentrism in sea level rise relocation and retreat policies in the

US. Glob. Environ. Change Policy Dimens., 49, 10–13, doi:10.1016/j.

gloenvcha.2018.01.002.

Marino, E. and H. Lazrus, 2015: Migration or forced displacement? The complex

choices of climate change and disaster migrants in Shishmaref, Alaska and

Nanumea, Tuvalu. Hum. Organ., 74(4), 341–350.

Marinucci, G.D., et al., 2014: Building resilience against climate effects—a

novel framework to facilitate climate readiness in public health agencies.

Int. J. Environ. Res. Public Health, 11(6), 6433–6458, doi:10.3390/

ijerph110606433.

Markandya, A., etal., 2018: Health co-beneﬁts from air pollution and mitigation

costs of the Paris Agreement: a modelling study. Lancet Planet. Health, 2(3),

e126–e133, doi:10.1016/s2542-5196(18)30029-9.

Markard, J. and D. Rosenbloom, 2020: A tale of two crises: COVID-19 and

climate. Sustain. Sci. Pract. Policy, 16(1), 53–60, doi:10.1080/15487733.20

20.1765679.

Markussen, T., M. Fibæk, F. Tarp and N.D.A. Tuan, 2018: The happy farmer: self-

employment and subjective well-being in rural Vietnam. J. Happiness Stud.,

19(6), 1613–1636, doi:10.1007/s10902-017-9858-x.

Marmot, M., etal., 2020: Health Equity in England: The Marmot Review 10

Years on. Institute of Health Equity, London.

Marselle, M., etal., 2020: Urban street tree biodiversity and antidepressant

prescriptions. Sci. Rep., 10(1), doi:10.1038/s41598-020-79924-5.

Martin-Shields, C.P. and W. Stojetz, 2019: Food security and conﬂict: empirical

challenges and future opportunities for research and policy making on food

security and conﬂict. World Dev., 119, 150–164.

Martin, L., etal., 2020: Nature contact, nature connectedness and associations

with health, wellbeing and pro-environmental behaviours. J. Environ.

Psychol., 68, doi:10.1016/j.jenvp.2020.101389.

Martinez-Solanas, E. and X. Basagana, 2019: Temporal changes in

temperature-related mortality in Spain and effect of the implementation of

a heat health prevention plan. Environ. Res., 169, 102–113, doi:10.1016/j.

envres.2018.11.006.

Martinez, G.S. and P. Berry, 2018: The Adaptation Health gap: a Global Overview,

Adaptation Gap Report 2018. United Nations Environment Programme

(unep), Nairobi, Kenya.

Martinez, G.S., etal., 2018: Cold-related mortality vs heat-related mortality in

a changing climate: a case study in Vilnius (Lithuania). Environ. Res., 166,

384–393, doi:10.1016/j.envres.2018.06.001.

Martinez, G.S., et al., 2019: Heat-health action plans in Europe: challenges

ahead and how to tackle them. Environ. Res., 176, doi:10.1016/j.

envres.2019.108548.

Mascarenhas, M., et al., 2018: A scoping review of published literature on

chikungunya virus. PLoS ONE, 13(11), e207554, doi:10.1371/journal.

pone.0207554.

Masselot, P., etal., 2019: Toward an improved air pollution warning system

in Quebec. Int. J. Environ. Res. Public Health, 16(12), doi:10.3390/

ijerph16122095.

Masson, V., A. Lemonsu and J. Voogt, 2018: The 9th International Conference

on Urban Climate. Urban Clim., 23, 1–7, doi:10.1016/j.uclim.2017.07.007.

Mastrorillo, M., et al., 2016: The inﬂuence of climate variability on internal

migration ﬂows in South Africa. Glob. Environ. Change, 39, 155–169.

Matsushita, N., etal., 2018: The non-linear and lagged short-term relationship

between rainfall and leptospirosis and the intermediate role of ﬂoods in

the Philippines. PLoS Negl. Trop. Dis., 12(4), e6331, doi:10.1371/journal.

pntd.0006331.

Matthew, R.A., 2018: Climate change adaptation and peacebuilding. In:

Routledge Handbook of Environmental Conﬂict and Peacebuilding.

Routledge, New York, pp. 108–120. ISBN 978-1315473772.

Matthews, T., R. Wilby and C. Murphy, 2019: An emerging tropical cyclone-

deadly heat compound hazard. Nat. Clim. Change, 9(8), 602, doi:10.1038/

s41558-019-0525-6.

Mayrhuber, E.A., etal., 2018: Vulnerability to heatwaves and implications for

public health interventions – a scoping review. Environ. Res., 166, 42–54,

doi:10.1016/j.envres.2018.05.021.

Mbereko, A., M.J. Chimbari, T. Manyangadze and S. Mukaratirwa, 2020:

Knowledge and perceptions of schistosomiasis, a water-borne disease, in

two semi-arid rural areas of South Africa (Ndumo) and Zimbabwe (Ntalale).

Food Waterborne Parasitol., 21, e91, doi:10.1016/j.fawpar.2020.e00091.

Mbow, C., C. Rosenzweig, L.G. Barioni, T.G. Benton, M. Herrero, M. Krishnapillai,

E. Liwenga, P. Pradhan, M.G. Rivera-Ferre, T. Sapkota, F.N. Tubiello, Y. Xu,

2019: Food Security. In: Climate Change and Land: an IPCC special report

on climate change, desertiﬁcation, land degradation, sustainable land

management, food security, and greenhouse gas ﬂuxes in terrestrial

ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O.

Pörtner, D.C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E.

Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E.

Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)]. Intergovernmental Panel

on Climate Change, Geneve.

McAllister, D.A., et al., 2019: Global, regional, and national estimates of

pneumonia morbidity and mortality in children younger than 5 years

between 2000 and 2015: a systematic analysis. Lancet Glob. Health, 7(1),

E47–E57, doi:10.1016/s2214-109x(18)30408-x.

1153

Health, Wellbeing and the Changing Structure of Communities Chapter 7

McCollum, D., etal., 2018: Connecting the sustainable development goals by

their energy inter-linkages. Environ. Res. Lett., 13(3), doi:10.1088/1748-

9326/aaafe3.

McCormack, M.C., et al., 2016: Respiratory effects of indoor heat and the

interaction with air pollution in chronic obstructive pulmonary disease.

Ann. Am. Thorac. Soc., 13(12), 2125–2131, doi:10.1513/AnnalsATS.201605-

329OC.

McCreesh, N. and M. Booth, 2014: The effect of increasing water temperatures

on Schistosoma mansoni transmission and Biomphalaria pfeifferi population

dynamics: an agent-based modelling study. Plos One, 9(7), doi:10.1371/

journal.pone.0101462.

McCreesh, N., G. Nikulin and M. Booth, 2015: Predicting the effects of climate

change on Schistosoma mansoni transmission in eastern Africa. Parasites

Vectors, 8, 4, doi:10.1186/s13071-014-0617-0.

McCubbin, S., B. Smit and T. Pearce, 2015: Where does climate ﬁt? Vulnerability

to climate change in the context of multiple stressors in Funafuti, Tuvalu.

Glob. Environ. Change, 30, 43–55.

McEvoy, D., 2019: Climate resilient urban development. Sustainability, 11(3),

doi:10.3390/su11030724.

McGregor, G., K. Ebi, B. Menne and P. Bessmoulin, 2015: Heatwaves and

Health Guidance on Warning System Development. World Meteorological

Organisation, Geneva.

McGregor, G.R., A. Bone and F. Pappenberger, 2017: Meteorological risk:

extreme temperatures. In: Science for Disaster Risk Management 2017:

Knowing Better and Losing Less [Poljanšek, K.(ed.)]. European Union,

Luxembourg, pp. 257–270.

McGregor, G.R. and K. Ebi, 2018: El Nino southern oscillation (ENSO) and

health: an overview for climate and health researchers. Atmosphere, 9(7),

32, doi:10.3390/atmos9070282.

McGuirk, E. and M. Burke, 2020: The economic origins of conﬂict in africa. J.

Polit. Econ., 128(10), 3940–3997, doi:10.1086/709993.

McIver, L., et al., 2016a: Health impacts of climate change in Paciﬁc island

countries: a regional assessment of vulnerabilities and adaptation priorities.

Environ. Health Perspect., 124(11), 1707–1714, doi:10.1289/ehp.1509756.

McIver, L.J., etal., 2016b: Review of climate change and water-related diseases

in Cambodia and ﬁndings from stakeholder knowledge assessments. Asia

Pac. J. Public Health, 28(2 Suppl), 49S–58S, doi:10.1177/1010539514558059.

McLeman, R., 2017: Thresholds in climate migration. Popul. Environ.,

doi:10.1007/s11111-017-0290-2.

McLeman, R., 2019: International migration and climate adaptation in an era of

hardening borders. Nat. Clim. Change, 9(12), 911–918, doi:10.1038/s41558-

019-0634-2.

McLeman, R., M. Mohammad and A. Nasima, 2017: Environmental inﬂuences

on skilled worker migration from Bangladesh to Canada. Can. Geogr., 62(3),

352–371, doi:10.1111/cag.12430.

McLeman, R., etal., 2021: Conceptual framing to link climate risk assessments

and climate-migration scholarship. Clim. Change, 165(24), doi:10.1007/

s10584-021-03056-6.

McMahon, S., P. Gamage and L.V. Fortington, 2021: Sports related heat

injury in Victoria, Australia: an analysis of 11years of hospital admission

and emergency department data. J. Sci. Med. Sport, 24(3), 224–228,

doi:10.1016/j.jsams.2020.08.011.

McMeeking, S. and C. Savage, 2020: Māori responses to Covid-19. Policy Q.,

16(3), 36–41, doi:10.26686/pq.v16i3.6553.

McMichael, C., 2015: Climate change-related migration and infectious disease.

Virulence, 6(6), 548–553.

McMichael, C., etal., 2021: Rising seas, immobilities, and translocality in small

island states: case studies from Fiji and Tuvalu. Popul. Environ., 43(1), 82–

107, doi:10.1007/s11111-021-00378-6.

McMichael, C., S. Dasgupta, S. Ayeb-Karlsson and I. Kelman, 2020: A review

of estimating population exposure to sea-level rise and the relevance for

migration. Environ. Res. Lett., 15(12), 123005, doi:10.1088/1748-9326/

abb398.

McMichael, C. and M. Katonivualiku, 2020: Thick temporalities of

planned relocation in Fiji. Geoforum, 108, 286–294, doi:10.1016/j.

geoforum.2019.06.012.

McMichael, C. and T. Powell, 2021: Planned relocation and health: a case

study from Fiji. Int. J. Environ. Res. Public Health, 18(8), doi:10.3390/

ijerph18084355.

McNamara, K., R. Bronen, N. Fernando and S. Klepp, 2018: The complex

decision-making of climate-induced relocation: adaptation and loss and

damage. Clim. Policy, 18(1), 111–117, doi:10.1080/14693062.2016.12488

86.

McNamara, K.E., 2015: Cross-border migration with dignity in Kiribati. Forced

Migr. Rev., 49, 62–62.

McNamara, K.E., R. Westoby and A. Chandra, 2021: Exploring climate-driven

non-economic loss and damage in the Paciﬁc Islands. Curr. Opin. Environ.

Sustain., 50, 1–11.

McPherson, M., et al., 2017: Expansion of the Lyme disease vector Ixodes

scapularis in Canada inferred from CMIP5 climate projections. Environ.

Health Perspect., 125(5), doi:10.1289/ehp57.

McTavish, R.K., et al., 2018: Association between high environmental heat

and risk of acute kidney injury among older adults in a northern climate:

a matched case-control study. Am. J. Kidney Dis., 71(2), 200–208,

doi:10.1053/j.ajkd.2017.07.011.

Medek, S., E. Danielle, J. Schwartz andS. S. Myers, 2017: Estimated effects of

future atmospheric CO2 concentrations on protein intake and the risk of

protein deﬁciency by country and region. Environ. Health Perspect., 125(8),

87002, doi:10.1289/ehp41.

Meehl, G.A., etal., 2018: Future heat waves and surface ozone. Environ. Res.

Lett., 13(6), doi:10.1088/1748-9326/aabcdc.

Meige, P., R. Blewitt and M.K. van Aalst, 2020: A Humanitarian Recipe for a

Green, Resilient and Inclusive Recovery from COVID-19. Solferino Academy,

Geneva.

Méndez-Lázaro, P.A., etal., 2018: Climate change, heat, and mortality in the

tropical urban area of San Juan, Puerto Rico. Int. J. Biometeorol., 62(5), 699–

707, doi:10.1007/s00484-016-1291-z.

Mendez, M., G. Flores-Haro and L. Zucker, 2020: The (in)visible victims of disaster:

understanding the vulnerability of undocumented Latino/a and indigenous

immigrants. Geoforum, 116, 50–62, doi:10.1016/j.geoforum.2020.07.007.

Menz, T. and H. Welsch, 2010: Population aging and environmental preferences

in OECD countries: the case of air pollution. Ecol. Econ., 69(12), 2582–2589,

doi:10.1016/j.ecolecon.2010.08.002.

Mercer, N. and M. Hanrahan, 2017: “Straight from the heavens into your

bucket”: domestic rainwater harvesting as a measure to improve water

security in a subarctic indigenous community. Int. J. Circumpolar Health,

76(1).DOI: https://doi.org/10.1080/22423982.2017.1312223

Merkens, J., etal., 2018: Regionalisation of population growth projections in

coastal exposure analysis. Clim. Change, 151(3-4), 413–426, doi:10.1007/

s10584-018-2334-8.

Merkord, C.L., etal., 2017: Integrating malaria surveillance with climate data

for outbreak detection and forecasting: the EPIDEMIA system. Malar. J.,

16(1), 89, doi:10.1186/s12936-017-1735-x.

Messeri, A., etal., 2021: A web survey to evaluate the thermal stress associated

with personal protective equipment among healthcare workers during

the COVID-19 pandemic in Italy. Int. J. Environ. Res. Public Health, 18(8),

doi:10.3390/ijerph18083861.

Messina, J., et al., 2019: The current and future global distribution and

population at risk of dengue. Nat. Microbiol., 4(9), 1508–1515, doi:10.1038/

s41564-019-0476-8.

Messina, J.P., et al., 2015: The many projected futures of dengue. Nat. Rev.

Microbiol, 13(4), 230–239, doi:10.1038/nrmicro3430.

Metallinou, M.-M. and T. Log, 2017: Health impacts of climate change-

induced subzero temperature ﬁres. Int. J. Environ. Res. Public Health, 14(7),

doi:10.3390/ijerph14070814.

1154

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Mezdour, A., L. Veronis and R. McLeman, 2015: Environmental Inﬂuences on

Haitian Migration to Canada and Connections to Social Inequality: Evidence

from Ottawa-Gatineau and Montreal [McLeman, R., J. Schade and T. Faist

(eds.)]. Springer, Dordrecht, 103–116.

Miao, Y.F., etal., 2017: Maternal exposure to ambient air temperature during

pregnancy and early childhood pneumonia. J. Therm. Biol., 69, 288–293,

doi:10.1016/j.jtherbio.2017.09.001.

Middleton, J., et al., 2020a: “We’re people of the snow:” weather, climate

change, and Inuit mental wellness. Soc. Sci. Med., 262, doi:10.1016/j.

socscimed.2020.113137.

Middleton, J., etal., 2020b: Indigenous mental health in a changing climate: a

systematic scoping review of the global literature. Environ. Res. Lett., 15(5),

doi:10.1088/1748-9326/ab68a9.

Midekisa, A., etal., 2015: Seasonal associations of climatic drivers and malaria

in the highlands of Ethiopia. Parasites Vectors, 8, doi:10.1186/s13071-015-

0954-7.

Milazzo, A., et al., 2016: Heatwaves differentially affect risk of Salmonella

serotypes. J. Infect., 73(3), 231–240, doi:10.1016/j.jinf.2016.04.034.

Miles-Novelo, A. and C. Anderson, 2019: Climate change and psychology:

effects of rapid global warming on violence and aggression. Curr. Clim.

Change Rep., 5(1), 36–46, doi:10.1007/s40641-019-00121-2.

Milner, J., etal., 2015: Health effects of adopting low greenhouse gas emission

diets in the UK. BMJ Open, 5(4), doi:10.1136/bmjopen-2014-007364.

Milner, J., etal., 2017: Projected health effects of realistic dietary changes to

address freshwater constraints in India: a modelling study. Lancet Planet.

Health, 1(1), e26–e32, doi:10.1016/S2542-5196(17)30001-3.

Miltenburg, E., etal., 2021: Seed Sowing: Indigenous Relationship-Building as

Processes of Environmental Action. Canadian Institute for Climate Choices,

Ottawa.

Miner, K.R., et al., 2018: Quantitative screening level assessment of human

risk from PCBs released in glacial meltwater: Silvretta Glacier, Swiss Alps.

Ecotoxicol. Environ. Saf., 166, 251–258, doi:10.1016/j.ecoenv.2018.09.066.

Mirasgedis, S., C. Tourkolias, E. Pavlakis and D. Diakoulaki, 2014: A

methodological framework for assessing the employment effects associated

with energy efﬁciency interventions in buildings. Energy Build., 82, 275–286,

doi:10.1016/j.enbuild.2014.07.027.

Miron, I.J., et al., 2015: Changes in cause-speciﬁc mortality during heat

waves in central Spain, 1975–2008. Int. J. Biometeorol., 59(9), 1213–1222,

doi:10.1007/s00484-014-0933-2.

Mirsaeidi, M., etal., 2016: Climate change and respiratory infections. Ann. ATS,

13(8), 1223–1230, doi:10.1513/AnnalsATS.201511-729 PS.

Mirumachi, N., A. Sawas and M. Workman, 2020: Unveiling the security concerns

of low carbon development: climate security analysis of the undesirable and

unintended effects of mitigation and adaptation. Clim. Dev., 12(2), 97–109.

Mishra, V., S. Mukherjee, R. Kumar and D.A. Stone, 2017: Heat wave exposure in

India in current, 1.5degrees C, and 2.0degrees C worlds. Environ. Res. Lett.,

12(12), doi:10.1088/1748-9326/aa9388.

Missirian, A. and W. Schlenker, 2017: Asylum applications respond to

temperature ﬂuctuations. Science, 358(6370), 1610–1614.

Mitchell, D., 2016: Human inﬂuences on heat-related health indicators

during the 2015 Egyptian heat wave. Bull. Am. Meteorol. Soc., 97, 70–S74,

doi:10.1175/bams-d-16-0132.1.

Mitchell, D., 2021: Climate attribution of heat mortality. Nat. Clim. Change,

11(6), 467–468, doi:10.1038/s41558-021-01049-y.

Mitchell, D., etal., 2018: Extreme heat-related mortality avoided under Paris

Agreement goals. Nat. Clim. Change, 8(7), 551–553, doi:10.1038/s41558-

018-0210-1.

Mitchell, D., etal., 2016: Attributing human mortality during extreme heat waves

to anthropogenic climate change. Environ. Res. Lett., 11, doi:10.1088/1748-

9326/11/7/074006.

Miyayo, S.F., P.O. Owili, M. A. Muga and T.H. Lin, 2021: Analysis of pneumonia

occurrence in relation to climate change in Tanga, Tanzania. Int. J. Environ.

Res. Public Health, 18(9), doi:10.3390/ijerph18094731.

Modenese, A., L. Korpinen and F. Gobba, 2018: Solar radiation exposure and

outdoor work: an underestimated occupational risk. Int. J. Environ. Res.

Public Health, 15(10), doi:10.3390/ijerph15102063.

Molina, O. and V. Saldarriaga, 2017: The perils of climate change: In utero

exposure to temperature variability and birth outcomes in the Andean

region. Econ. Hum. Biol., 24, 111–124.

Monaghan, A., etal., 2018: The potential impacts of 21st century climatic and

population changes on human exposure to the virus vector mosquito Aedes

aegypti. Clim. Change, 146(3), 487–500, doi:10.1007/s10584-016-1679-0.

Monaghan, A.J., etal., 2015: Climate change inﬂuences on the annual onset

of Lyme disease in the United States. Ticks Tick Borne Dis., 6(5), 615–622,

doi:10.1016/j.ttbdis.2015.05.005.

Monaghesh, E. and A. Hajizadeh, 2020: The role of telehealth during COVID-19

outbreak: a systematic review based on current evidence. BMC Public

Health, 20(1), doi:10.1186/s12889-020-09301-4.

Monchatre-Leroy, E., et al., 2017: Spatial and temporal epidemiology of

nephropathia epidemica incidence and hantavirus seroprevalence in

rodent hosts: identiﬁcation of the main environmental factors in Europe.

Transbound. Emerg. Dis., 64(4), 1210–1228, doi:10.1111/tbed.12494.

Moore, F.C., N. Obradovich, F. Lehner and P. Baylis, 2019: Rapidly declining

remarkability of temperature anomalies may obscure public perception of

climate change. Proc. Natl. Acad. Sci., 116(11), 4905–4910.

Moore, S., etal., 2018: Dynamics of cholera epidemics from Benin to Mauritania.

PLoS Negl. Trop. Dis., 12(4), e6379, doi:10.1371/journal.pntd.0006379.

Moore, S.M., et al., 2017: El Niño and the shifting geography of cholera in

Africa. Proc. Natl. Acad. Sci. U. S. A., 114(17), 4436–4441, doi:10.1073/

pnas.1617218114.

Mora, C., C.W.Counsell, C.R. Bielecki and L.V. Louis, 2017a: Twenty-

seven ways a heat wave can kill you: Deadly Heat in the Era of Climate

Change. Circ. Cardiovasc. Qual. Outcomes, 10(11), e4233, doi:10.1161/

circoutcomes.117.004233.

Mora, C., etal., 2017b: Global risk of deadly heat. Nat. Clim. Change, 7(7), 501,

doi:10.1038/nclimate3322.

Morabito, M., etal., 2021: Heat-related productivity loss: beneﬁts derived by

working in the shade or work-time shifting. Int. J. Prod. Perform. Manag.,

70(3), 507–525, doi:10.1108/ijppm-10-2019-0500.

Moreﬁeld, P.E., etal., 2018: Heat-related health impacts under scenarios of

climate and population change. Int. J. Environ. Res. Public Health, 15(11),

doi:10.3390/ijerph15112438.

Morens, D.M. and A.S. Fauci, 2014: Chikungunya at the door – deja vu all over

again? N. Engl. J. Med., 371(10), 885–887, doi:10.1056/NEJMp1408509.

Moretti, A., M. Pascale and A. F. Logrieco, 2019: Mycotoxin risks under a

climate change scenario in Europe. Trends Food Sci. Technol., 84, 38–40,

doi:10.1016/j.tifs.2018.03.008.

Morgan, A., A. Ross and N. Reavley, 2018: Systematic review and meta-analysis

of mental health ﬁrst aid training: effects on knowledge, stigma, and helping

behaviour. PLoS ONE, 13(5), doi:10.1371/journal.pone.0197102.

Morin, C., etal., 2018: Unexplored opportunities: use of climate- and weather-

driven early warning systems to reduce the burden of infectious diseases.

Curr. Environ. Health Rep., 5(4), 430–438, doi:10.1007/s40572-018-0221-0.

Morris, N.B., etal., 2020: Sustainable solutions to mitigate occupational heat

strain – an umbrella review of physiological effects and global health

perspectives. Environ. Health, 19(1), doi:10.1186/s12940-020-00641-7.

Morton, S., D. Pencheon and G. Bickler, 2019: The sustainable development

goals provide an important framework for addressing dangerous climate

change and achieving wider public health beneﬁts. Public Health, 174, 65–

68, doi:10.1016/j.puhe.2019.05.018.

Mosello, B., C. König, A. Day and C. Nagarajan, 2021: Addressing Climate-

related security Risks: Towards a Programme for Action. adelphi, Berlin.

Moss, R.H., P.M. Reed, A. Hadjimichael and J. Rozenberg, 2021: Planned

relocation: pluralistic and integrated science and governance. Science,

372(6548), 1276–1279. Available at: http://science.sciencemag.org/

content/372/6548/1276.abstrac.

1155

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Mosuela, C., etal., 2015: The Role of A Priori Cross-Border Migration after

Extreme Climate Events: The Case of the Philippines after Typhoon Haiyan.

In: Environmental Change, Adaptation and Migration: Bringing in the

Region, Palgrave Macmillan, London. pp. 98–116.

Mousavi, A., etal., 2020: Climate change and health in Iran: a narrative review.

J. Environ. Health Sci. Eng., 18(1), 367–378, doi:10.1007/s40201-020-

00462-3.

Mpandeli, S., et al., 2018: Climate change adaptation through the water-

energy-food nexus in Southern Africa. Int. J. Environ. Res. Public Health,

15(10), doi:10.3390/ijerph15102306.

Mt. Pleasant, J., 2016: Food yields and nutrient analyses of the three sisters: a

Haudenosaunee cropping system. Ethnobiol. Lett., 7(1), 87-98.

Muehe, E., et al., 2019: Rice production threatened by coupled stresses of

climate and soil arsenic. Nat. Commun., 10, doi:10.1038/s41467-019-

12946-4.

Mueller, V., G. Sheriff, X. Dou and C. Gray, 2020: Temporary migration and

climate variation in eastern Africa. World Dev., 126, 104704. Available at:

http://www.sciencedirect.com/science/article/pii/S0305750X19303523.

Mugambiwa, S., 2018: Adaptation measures to sustain indigenous practices

and the use of indigenous knowledge systems to adapt to climate change

in Mutoko rural district of Zimbabwe. Jamba J. Disaster Risk Stud., 10,

doi:10.4102/jamba.v10i1.388.

Mukeka, J., J. Ogutu, E. Kanga and E. Roskaft, 2019: Human-wildlife conﬂicts

and their correlates in Narok County, Kenya. Glob. Ecol. Conserv., 18,

doi:10.1016/j.gecco.2019.e00620.

Mukherjee, A., S. Babu and S. Ghosh, 2020: Thinking about water and air to

attain sustainable development goals during times of COVID-19 pandemic.

J. Earth Syst. Sci., 129(1), doi:10.1007/s12040-020-01475-0.

Mullins, J. and C. White, 2019: Temperature and mental health: evidence from

the spectrum of mental health outcomes. J. Health Econ., 68, doi:10.1016/j.

jhealeco.2019.102240.

Murdock, C.C., E.D. Sternberg and M.B. Thomas, 2016: Malaria transmission

potential could be reduced with current and future climate change. Sci. Rep.,

6, 27771, doi:10.1038/srep27771.

Murphy, H.M., K.D. Pintar, E.A. McBean and M.K. Thomas, 2014: A systematic

review of waterborne disease burden methodologies from developed

countries. J. Water Health, 12(4), 634–655, doi:10.2166/wh.2014.049.

Murray-Tortarolo, G.N. and S.M. Martnez, 2021: Drought as a driver of Mexico-

US migration. Clim. Change, 164(3), 48, doi:10.1007/s10584-021-03030-2.

Murray, C.J.L., etal., 2020: Global burden of 87 risk factors in 204countries

and territories, 1990–2019: a systematic analysis for the Global Burden of

Disease Study 2019. Lancet, 396(10258), 1223–1249.

Murthy, B.P., etal., 2017: Progress in public health emergency preparedness—

United States, 2001–2016. Am. J. Public Health, 107(S2), S180–S185,

doi:10.2105/ajph.2017.304038.

Musacchio, A., etal., 2021: Planning for the health impacts of climate change:

ﬂooding, private groundwater contamination and waterborne infection

– a cross-sectional study of risk perception, experience and behaviours

in the Republic of Ireland. Environ. Res., 194, 110707, doi:10.1016/j.

envres.2021.110707.

Mutheneni, S.R., A.P. Morse, C. Caminade and S.M. Upadhyayula, 2017: Dengue

burden in India: recent trends and importance of climatic parameters. Emerg.

Microbes Infect., 6(8), e70, doi:10.1038/emi.2017.57.

Mutono, N., etal., 2020: The nexus between improved water supply and water-

borne diseases in urban areas in Africa: a scoping review protocol. AAS Open

Res., 3, 12, doi:10.12688/aasopenres.13063.2.

Mutono, N., etal., 2021: The nexus between improved water supply and water-

borne diseases in urban areas in Africa: a scoping review. AAS Open Res, 4,

27, doi:10.12688/aasopenres.13225.1.

Muyambo, F., A. Jordaan and Y. Bahta, 2017: Assessing social vulnerability to

drought in South Africa: policy implication for drought risk reduction. Jamba

J. Disaster Risk Stud., 9(1), doi:10.4102/jamba.v9i1.326.

Mweya, C., etal., 2016: Climate change inﬂuences potential distribution of

infected Aedes aegypti co-occurrence with dengue epidemics risk areas in

Tanzania. PLoS ONE, 11(9), doi:10.1371/journal.pone.0162649.

Mygind, L., etal., 2019: Immersive nature-experiences as health promotion

interventions for healthy, vulnerable, and sick populations? A systematic

review and appraisal of controlled studies. Front. Psychol., 10, 943–943,

doi:10.3389/fpsyg.2019.00943.

Myrttinen, H., L. Khattab and C. Maydaa, 2017: Trust no one, beware of

everyone. In: A Gendered Approach to the Syrian Refugee Crisis. Routledge,

Abingdon, Oxon; New York, NY, pp. 61–76.

Myrttinen, H., J. Naujoks and J. Schilling, 2015: Gender, natural resources, and

peacebuilding in Kenya and Nepal. Peace Rev., 27(2), 181–187, doi:10.1080

/10402659.2015.1037623.

Na, M., etal., 2020: Food insecurity and cognitive function in middle to older

adulthood: a systematic review. Adv. Nutr., 11(3), 667–676, doi:10.1093/

advances/nmz122.

Nagarajan, C., et al., 2018: Climate Fragility Proﬁle: Lake Chad Basilisks.

Adelphi, Berlin.

Nagy, G.J., etal., 2018: An assessment of the relationships between extreme

weather events, vulnerability, and the impacts on human wellbeing in Latin

America. Int. J. Environ. Res. Public Health, 15, 1802.

Nah, K., A. Bede-Fazekas, A. Trajer and J. Wu, 2020: The potential impact of

climate change on the transmission risk of tick-borne encephalitis in

Hungary. BMC Infect Dis., 20(1), doi:10.1186/s12879-019-4734-4.

Nahian, M. A., , etal., 2018: Drinking water salinity associated health crisis in

coastal Bangladesh. Elem. Sci. Anthropocene, 6.DOI: https://doi.org/10.1525/

elementa.143

Naik, V., S. Szopa, B. Adhikary, P. Artaxo, T. Berntsen, W.D. Collins, S. Fuzzi, L.

Gallardo, A. Kiendler Scharr, Z. Klimont, H. Liao, N. Unger and P. Zanis, 2021:

Short-Lived Climate Forcers. In: Climate Change 2021: The Physical Science

Basis. Contribution of Working Group I to the Sixth Assessment Report of the

Intergovernmental Panel on Climate Change. [Masson-Delmotte, V., P. Zhai,

A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.

I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T.

Waterﬁeld, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press,

Cambridge..

Nasir, S., et al., 2017: Effect of climatic conditions and water bodies on

population dynamics of the dengue vector, Aedes aegypti (diptera: culicidae).

J. Arthropod Borne Dis., 11(1), 50–59.

Natarajan, N., K. Brickell and L. Parsons, 2019: Climate change adaptation and

precarity across the rural–urban divide in Cambodia: towards a ‘climate

precarity’ approach. Environ. Plan. E Nat. Space, 2(4), 899–921.

Navarro, J., et al., 2016: Children’s disaster experiences and psychological

symptoms: an international comparison between the Chilean earthquake

and Tsunami and Hurricane Katrina. Int. Soc. Work, 59(4), 545–558.

Navarro, K., etal., 2019: Wildland ﬁreﬁghter smoke exposure and risk of lung

cancer and cardiovascular disease mortality. Environ. Res., 173, 462–468,

doi:10.1016/j.envres.2019.03.060.

Navarro, L., etal., 2017: Monitoring biodiversity change through effective global

coordination. Curr. Opin. Environ. Sustain., 29, 158–169, doi:10.1016/j.

cosust.2018.02.005.

Navarro, O., etal., 2021: Coping strategies regarding coastal ﬂooding risk in

a context of climate change in a French Caribbean island. Environ. Behav.,

53(6), 636–660, doi:10.1177/0013916520916253.

Navi, M., etal., 2017: Developing health-related indicators of climate change:

Australian stakeholder perspectives. Int. J. Environ. Res. Public Health, 14(5),

doi:10.3390/ijerph14050552.

Nawrotzki, R. and J. DeWaard, 2018: Putting trapped populations into place:

climate change and inter-district migration ﬂows in Zambia. Reg. Environ.

Change, 18(2), 533–546, doi:10.1007/s10113-017-1224-3.

Nawrotzki, R.J. and M. Bakhtsiyarava, 2017: International climate migration:

evidence for the climate inhibitor mechanism and the agricultural pathway.

Popul. Space Place, 23(4), e2033–e2033.

1156

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Nawrotzki, R.J., J. DeWaard, M. Bakhtsiyarava and J.T. Ha, 2017: Climate shocks

and rural-urban migration in Mexico: exploring nonlinearities and thresholds.

Clim. Change, 140(2), 243–258, doi:10.1007/s10584-016-1849-0.

Neef, A., etal., 2018: Climate adaptation strategies in Fiji: the role of social

norms and cultural values. World Dev., 107, 125–137, doi:10.1016/j.

worlddev.2018.02.029.

Nelson, M., et al., 2016: Alignment of healthy dietary patterns and

environmental sustainability: a systematic review. Adv. Nutr., 7(6), 1005–

1025, doi:10.3945/an.116.012567.

Neufeld, H.T., C. Richmond and Southwest Ontario Aboriginal Health Access

Centre, , 2020: Exploring ﬁrst nation elder women’s relationships with food

from social, ecological, and historical perspectives. Curr. Dev. Nutr., 4(3),

nzaa11, doi:10.1093/cdn/nzaa011.

Neumann, B., A.T. Vafeidis, J. Zimmermann and R.J. Nicholls, 2015: Future coastal

population growth and exposure to sea-level rise and coastal ﬂooding – a

global assessment. PLoS ONE, 10(6), e131375–e131375.

Neumann, J.E., etal., 2019: Estimates of present and future asthma emergency

department visits associated with exposure to oak, birch, and grass pollen

in the United States. GeoHealth, 3(1), 11–27, doi:10.1029/2018gh000153.

Neumann, K. and F. Hermans, 2015: What drives human migration in Sahelian

countries? A meta-analysis. Popul. Space Place, doi:10.1002/psp.1962.

Newnham, E., N. Titov and P. McEvoy, 2020: Preparing mental health systems

for climate crisis. Lancet Planet. Health, 4(3), E89–E90.

Ng, K.Y.Y., etal., 2021: Factors inﬂuencing protective behaviours during haze

episodes in Singapore: a population-based study. Ann. Acad. Med. Singap.,

50(7), 514–526, doi:10.47102/annals-acadmedsg.2020614.

Ngigi, M., U. Mueller and R. Birner, 2017: Gender differences in climate

change adaptation strategies and participation in group-based approaches:

an intra-household analysis from rural Kenya. Ecol. Econ., 138, 99–108,

doi:10.1016/j.ecolecon.2017.03.019.

Ngo, C., P. Poortvliet and P. Feindt, 2020: Drivers of ﬂood and climate change

risk perceptions and intention to adapt: an explorative survey in coastal and

delta Vietnam. J. Risk Res., 23(4), 424–446, doi:10.1080/13669877.2019.1

591484.

Ngo, N.S. and R.M. Horton, 2016: Climate change and fetal health: the impacts

of exposure to extreme temperatures in New York City. Environ. Res., 144(Pt

A), 158–164, doi:10.1016/j.envres.2015.11.016.

Nguyen, H., 2019: Gendered vulnerabilities in times of natural disasters: male-to-

female violence in the Philippines in the aftermath of super Typhoon Haiyan.

Violence Against Women, 25(4), 421–440, doi:10.1177/1077801218790701.

Nhamo, G. and S. Muchuru, 2019: Climate adaptation in the public health

sector in Africa: evidence from United Nations framework convention on

climate change national communications. Jamba J. Disaster Risk Stud., 11,

doi:10.4102/jamba.v11i1.644.

Nicholls, R.J., etal., 2021: A global analysis of subsidence, relative sea-level

change and coastal ﬂood exposure. Nat. Clim. Change, 11(4), 338–342,

doi:10.1038/s41558-021-00993-z.

Nicholls, R.J., etal., 2007: Ranking of the World’s Cities Most Exposed to Coastal

Flooding Today and in the Future. Organisation for Economic Co-operation

and Development (OECD), Paris.

Nichols, A., 2019: Climate change, natural hazards, and relocation: insights

from Nabukadra and Navuniivi villages in Fiji. Clim. Change, 156(1), 255–

271, doi:10.1007/s10584-019-02531-5.

Nichols, G., I. Lake and C. Heaviside, 2018: Climate change and water-related

infectious diseases. Atmosphere, 9(10), doi:10.3390/atmos9100385.

Nightingale, A., 2017: Power and politics in climate change adaptation efforts:

struggles over authority and recognition in the context of political instability.

Geoforum, 84, 11–20.

Niles, M., et al., 2021: Climate impacts associated with reduced diet

diversity in children across nineteen countries. Environ. Res. Lett., 16(1),

doi:10.1088/1748-9326/abd0ab.

Nilsson, L., etal., 2011: Diet and lifestyle of the Sami of southern Lapland in

the 1930s–1950s and today. Int. J. Circumpolar Health, 70(3), 301–318,

doi:10.3402/ijch.v70i3.17831.

Nitschke, M., etal., 2016: Evaluation of a heat warning system in Adelaide,

South Australia, using case-series analysis. BMJ Open, 6(7), doi:10.1136/

bmjopen-2016-012125.

Nkumama, I.N., W.P. O’Meara and F.H.A. Osier, 2017: Changes in malaria

epidemiology in Africa and new challenges for elimination. Trends Parasitol.,

33(2), 128–140, doi:10.1016/j.pt.2016.11.006.

Noelke, C., etal., 2016: Increasing ambient temperature reduces emotional

well-being. Environ. Res., 151, 124–129, doi:10.1016/j.envres.2016.06.045.

Nordqvist, P. and F. Krampe, 2018: Climate Change and Violent Conﬂict: Sparse

Evidence from South Asia and South East Asia. International Peace Research

Institute, Stockholm.

Norton-Smith, K., et al., 2016: Climate Change and Indigenous Peoples: a

Synthesis of Current Impacts and Experiences. US Department of Agriculture,

Forest Service, Paciﬁc Northwest Research Station, Portland.

Nurse, K., 2019: Migration, diasporas, remittances and the sustainable

development goals in least developed countries. J. Glob. Dev., 9(2), 20190006.

Nursey-Bray, M., 2017: Towards socially just adaptive climate governance: the

transformative potential of conﬂict. Local Environ, 22(2), 156–171, doi:10.1

080/13549839.2016.1181618.

Nursey-Bray, M. and R. Palmer, 2018: Country, climate change adaptation and

colonisation: insights from an Indigenous adaptation planning process,

Australia. Heliyon, 4(3), e565, doi:10.1016/j.heliyon.2018.e00565.

Nursey-Bray, M., et al., 2020: Scale, colonisation and adapting to climate

change: insights from the Arabana people, South Australia. Geoforum, 114,

138–150, doi:10.1016/j.geoforum.2020.05.021.

O’Neill, D., A. Fanning, W. Lamb and J. Steinberger, 2018: A good life for all

within planetary boundaries. Nat. Sustain., 1(2), 88–95, doi:10.1038/

s41893-018-0021-4.

O’Donnell, R., M. Savaglio, D. Vicary and H. Skouteris, 2021: Effect of community

mental health care programs in Australia: a systematic review. Aust. J. Prim.

Health, 26(6), 443–451.

Oakes, R., 2019: Culture, climate change and mobility decisions in Paciﬁc Small

Island Developing States. Popul. Environ., 40(4), 480–503, doi:10.1007/

s11111-019-00321-w.

Obaidat, M.M. and A.A. Roess, 2018: First report on seroprevalence and risk

factors of dengue virus in Jordan. Trans. R. Soc. Trop. Med. Hyg., 112(6),

279–284, doi:10.1093/trstmh/try055.

Obokata, R. and L. Veronis, 2018: Transnational approaches to remittances,

risk reduction, and disaster relief: evidence from post-Typhoon Haiyan

experiences of Filipino immigrants in Canada. In Routledge handbook

of environmental displacement and migration.[McLeman, R. and F.

Gemenne(eds.)]. Routledge, London.

Obradovich, N. and J.H. Fowler, 2017: Climate change may alter human physical

activity patterns. Nat. Hum. Behav., 1, 97, doi:10.1038/s41562-017-0097.

https://www.nature.com/articles/s41562-017-0097#supplementary-

information.

Obradovich, N. and R. Migliorini, 2018: Sleep and the human impacts of climate

change. Sleep Med. Rev., 42, 1–2, doi:10.1016/j.smrv.2018.09.002.

Obradovich, N., R. Migliorini, S.C. Mednick and J.H. Fowler, 2017: Nighttime

temperature and human sleep loss in a changing climate. Sci. Adv., 3(5),

e1601555–e1601555, doi:10.1126/sciadv.1601555.

Obradovich, N., R. Migliorini, M.P. Paulus and I. Rahwan, 2018: Empirical

evidence of mental health risks posed by climate change. Proc. Natl. Acad.

Sci. U.S.A., 115(43), 10953–10958, doi:10.1073/pnas.1801528115.

Obradovich, N. and I. Rahwan, 2019: Risk of a feedback loop between climatic

warming and human mobility. J. R. Soc. Interface.16, DOI: 2019005820190058

Ocello, C., A. Petrucci, M.R. Testa and D. Vignoli, 2015: Environmental aspects of

internal migration in Tanzania. Popul. Environ., 37(1), 99–108.

1157

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Ogunbode, C., et al., 2021: Negative emotions about climate change are

related to insomnia symptoms and mental health: cross-sectional evidence

from 25countries. Curr. Psychol., doi:10.1007/s12144-021-01385-4.

Ogunbode, C.A., C. Demski, S.B. Capstick and R.G. Sposato, 2019: Attribution

matters: revisiting the link between extreme weather experience and climate

change mitigation responses. Glob. Environ. Change, 54, 31–39.

Okpara, U.T., L.C. Stringer and A.J. Dougill, 2016: Perspectives on contextual

vulnerability in discourses of climate conﬂict. Earth Syst. Dyn., 7(1), 89–102,

doi:10.5194/esd-7-89-2016.

Okpara, U.T., L.C. Stringer and A.J. Dougill, 2018: Integrating climate adaptation,

water governance and conﬂict management policies in lake riparian zones:

insights from African drylands. Environ. Sci. Policy, 79, 36–44.

Olson, D.M. and G.A.S. Metz, 2020: Climate change is a major stressor

causing poor pregnancy outcomes and child development. F1000Res, 9,

doi:10.12688/f1000research.27157.1.

Oluwole, O.S.A., 2015: Seasonal inﬂuenza epidemics and El Ninos. Front. Public

Health, 3, doi:10.3389/fpubh.2015.00250.

Oluwole, O.S.A., 2017: Dynamic regimes of El Nino southern oscillation

and inﬂuenza pandemic timing. Front. Public Health, 5, doi:10.3389/

fpubh.2017.00301.

Onozuka, D. and A. Hagihara, 2015: Non-stationary dynamics of climate

variability in synchronous inﬂuenza epidemics in Japan. Int. J. Biometeorol.,

59(9), 1253–1259, doi:10.1007/s00484-014-0936-z.

Opondo, M., U. Abdi and P. Nangiro, 2016: Assessing Gender in Resilience

Programming: Uganda. BRACED, Overseas Development Institute, London.

16 pp.

Oppermann, E., et al., 2021: Establishing intensifying chronic exposure to

extreme heat as a slow onset event with implications for health, wellbeing,

productivity, society and economy. Curr. Opin. Environ. Sustain., 50, 225–

235, doi:10.1016/j.cosust.2021.04.006.

Orlov, A., etal., 2020: Economic costs of heat-induced reductions in worker

productivity due to global warming. Glob. Environ. Change Policy Dimensions,

63, doi:10.1016/j.gloenvcha.2020.102087.

Orru, H., et al., 2019: Ozone and heat-related mortality in Europe in 2050

signiﬁcantly affected by changes in climate, population and greenhouse gas

emission. Environ. Res. Lett., 14(7), doi:10.1088/1748-9326/ab1cd9.

Orru, H., K.L. Ebi and B. Forsberg, 2017: The interplay of climate change and air

pollution on health. Curr. Environ. Health Rep., 4(4), 504–513, doi:10.1007/

s40572-017-0168-6.

Orru, K., etal., 2016: Well-being and environmental quality: Does pollution

affect life satisfaction? Qual. Life Res., 25(3), 699–705, doi:10.1007/s11136-

015-1104-6.

Osberghaus, D. and J. Kühling, 2016: Direct and indirect effects of weather

experiences on life satisfaction–which role for climate change expectations?

J. Environ. Plan. Manag., 59(12), 2198–2230.

Ostergard, J.R.L. and N. Seltzer, 2020: Climate change and global health

security: an overview of the scope and issues. Glob. Health Gov., X (1) 5–23.

Ostro, B., etal., 2010: The effects of temperature and use of air conditioning

on hospitalizations. Am. J. Epidemiol., 172(9), 1053–1061, doi:10.1093/aje/

kwq231.

Ottenhoff, L., 2021: Indigenous conservation can get Canada to climate goals:

former MP Ethel Blondin-Andrew to Trudeau. National Observer, 25 January

2021. https://www.nationalobserver.com/2021/01/25/news/climate-

change-indigenous-knowledge-ethel-blondin-andrew. Last accessed 3

September 2021.

Otto, F.E.L., etal., 2020: Challenges to understanding extreme weather changes

in lower income countries. Bull. Am. Meteorol. Soc., 101(10), E1851–E1860,

doi:10.1175/bams-d-19-0317.1.

Otto, I.M., et al., 2017: Social vulnerability to climate change: a review of

concepts and evidence. Reg. Environ. Change, 17(6), 1651–1662.

Ouattara, B. and E. Strobl, 2014: Hurricane strikes and local migration in US

coastal counties. Econ. Lett., 124(1), 17–20.

Owain, E.L. and M. A. Maslin, 2018: Assessing the relative contribution of

economic, political and environmental factors on past conﬂict and the

displacement of people in East Africa. Palgrave Commun., 4, doi:10.1057/

s41599-018-0096-6.

Owen, J.P., B. Baig, C. Abbo and Y. Baheretibeb, 2016: Child and adolescent

mental health in sub-Saharan Africa: a perspective from clinicians and

researchers. BJPsych Int., 13, 45–47, doi:10.1192/s2056474000001136.

Paavola, J., 2017: Health impacts of climate change and health and social

inequalities in the UK. Environ. Health, 16(Suppl 1), 113, doi:10.1186/

s12940-017-0328-z.

Paerl, H.W., et al., 2016: Mitigating cyanobacterial harmful algal blooms

in aquatic ecosystems impacted by climate change and anthropogenic

nutrients. Harmful Algae, 54, 213–222, doi:10.1016/j.hal.2015.09.009.

Paffenholz, T., 2018: Women in peace negotiations. In: Gendering Diplomacy

and International Negotiation. Springer, Dordrecht, pp. 169–191.

Pai, N., 2008: Climate Change and National Security: Preparing India for New

Conﬂict Scenarios. Indian National Interest Policy Brief, Vol. 1. Takshashila

Institution, Bangalore.

Pal, J.S. and E.A.B. Eltahir, 2016: Future temperature in southwest Asia projected

to exceed a threshold for human adaptability. Nat. Clim. Change, 6(2), 197–

200, doi:10.1038/nclimate2833.

Papadakis, G., Z. Chalabi and J.E. Thornes, 2018: Ambulance Service Resource

Planning for Extreme Temperatures: Analysis of Ambulance 999 Calls

during Episodes of Extreme Temperature in London, UK. Atmosphere, 9(5),

doi:10.3390/atmos9050182.

Park, C., etal., 2019: The global effect of extreme weather events on nutrient

supply: a superposed epoch analysis. Lancet Planet. Health, 3, e429–e438,

doi:10.1016/s2542-5196(19)30193-7.

Park, J., etal., 2020: Association between short-term air pollution exposure and

attention-deﬁcit/hyperactivity disorder-related hospital admissions among

adolescents: a nationwide time-series study. Environ. Pollut., 266(Pt 1),

115369, doi:10.1016/j.envpol.2020.115369.

Park, M., K. Park and G. Bahk, 2018a: Combined inﬂuence of multiple climatic

factors on the incidence of bacterial foodborne diseases. Sci. Total Environ.,

610, 10–16, doi:10.1016/j.scitotenv.2017.08.045.

Park, M.S., K.H. Park and G.J. Bahk, 2018b: Combined inﬂuence of multiple

climatic factors on the incidence of bacterial foodborne diseases. Sci. Total

Environ., 610-611, 10–16, doi:10.1016/j.scitotenv.2017.08.045.

Park, M.S., K.H. Park and G.J. Bahk, 2018c: Interrelationships between multiple

climatic factors and incidence of foodborne diseases. Int. J. Environ. Res.

Public Health, 15(11), doi:10.3390/ijerph15112482.

Park, R.J., A.P. Behrer and J. Goodman, 2021: Learning is inhibited by heat

exposure, both internationally and within the United States. Nat. Hum.

Behav., 5(1), 19–27, doi:10.1038/s41562-020-00959-9.

Parks, R.M., etal., 2020: Anomalously warm temperatures are associated with

increased injury deaths. Nat. Med., 26(1), 65–70.

Parthemore, C., F. Femia and C. Werrell, 2018: The global responsibility to

prepare for intersecting climate and nuclear risks. Bull. At. Sci., 74(6), 374–

378, doi:10.1080/00963402.2018.1533186.

Pascal, M., etal., 2021: Extreme heat and acute air pollution episodes: a need

for joint public health warnings? Atmos. Environ., 249, doi:10.1016/j.

atmosenv.2021.118249.

Pasini, A. and F. Mazzocchi, 2020: Perception and risk of Covid-19 and climate

change: investigating analogies in a common framework. Glob. Sustain., 3,

e32, doi:10.1017/sus.2020.30.

Patel, D., etal., 2019: Joint effects of heatwaves and air quality on ambulance

services for vulnerable populations in Perth, western Australia. Environ.

Pollut., 252, 532–542, doi:10.1016/j.envpol.2019.05.125.

Patel, P., S. Mondal and K. Ghosh, 2020: Some respite for India’s dirtiest river?

Examining the Yamuna’s water quality at Delhi during the COVID-19 lockdown

period. Sci. Total Environ., 744, doi:10.1016/j.scitotenv.2020.140851.

Patterson, K., etal., 2017: Seasonal variation of food security among the Batwa

of Kanungu, Uganda. Public Health Nutr., 20(1), 1-11.

1158

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Paudel, B., etal., 2021: Increased duration of pollen and mold exposure are

linked to climate change. Sci. Rep., 11(1), 12816–12816, doi:10.1038/

s41598-021-92178-z.

Payne, J., D. Weatherall and F. Downy, 2015: Capturing the “multiple beneﬁts”

of energy efﬁciency in practice: the UK example. In: ECEE Summer Study

Proc., pp. 229–238.

Paynter, S., et al., 2010: Childhood pneumonia: a neglected, climate-

sensitive disease? Lancet, 376(9755), 1804–1805, doi:10.1016/s0140-

6736(10)62141-1.

Paz, S. and J.C. Semenza, 2016: El Niño and climate change – contributing

factors in the dispersal of Zika virus in the Americas? Lancet, 387(10020),

745, doi:10.1016/s0140-6736(16)00256-7.

Pearse, R., 2017: Gender and climate change. Wiley Interdiscip. Rev. Clim.

Change, 8(2), e451.

Pelling, M., et al., 2021: Synergies between COVID-19 and climate change

impacts and responses. J. Extrem. Events. DOI:https://doi.org/10.1142/

S2345737621310023

Pelling, M. and K. Dill, 2010: Disaster politics: tipping points for change in the

adaptation of sociopolitical regimes. Prog. Hum. Geogr., 34(1), 21–37.

Pena-Garcia, V. H., O. Triana-Chavez and S. Arboleda-Sanchez, 2017: Estimating

effects of temperature on dengue transmission in Colombian cities. Ann.

Glob. Health, 83(3), 509–518, doi:10.1016/j.aogh.2017.10.011.

Peng, W., J. Yang, X. Lu and D.L. Mauzerall, 2018: Potential co-beneﬁts of

electriﬁcation for air quality, health, and CO2 mitigation in 2030 China. Appl.

Energy, 218, 511–519, doi:10.1016/j.apenergy.2018.02.048.

Peng, W., J. Yang, F. Wagner and L. Mauzerall, 2017: Substantial air quality

and climate co-beneﬁts achievable now with sectoral mitigation

strategies in China. Sci. Total Environ., 598, 1076–1084, doi:10.1016/j.

scitotenv.2017.03.287.

Perera, F., 2017: Multiple threats to child health from fossil fuel combustion:

impacts of air pollution and climate change. Environ. Health Perspect.,

125(2), 141–148, doi:10.1289/ehp299.

Perevolotsky, A. and E. Sheffer, 2011: Integrated management of heterogenous

landscape – Mediterranean Israel as a study case. Isr. J. Ecol. Evol., 57(1-2),

111–128.

Peters, D.H., etal., 2019: Financing common goods for health: core government

functions in health emergency and disaster risk management. Health Syst.

Reform, 5(4), 307–321, doi:10.1080/23288604.2019.1660104.

Petkova, E.P., A. Gasparrini and P.L. Kinney, 2014: Heat and mortality in New

York City since the beginning of the 20th century. Epidemiology, 25(4), 554–

560, doi:10.1097/ede.0000000000000123.

Petrova, D., etal., 2019: Sensitivity of large dengue epidemics in Ecuador to long-

lead predictions of El Nino. Clim. Serv., 15, doi:10.1016/j.cliser.2019.02.003.

Petrova, K., 2021: Natural hazards, internal migration and protests in

Bangladesh. J. Peace Res., 58(1), 33–49, doi:10.1177/0022343320973741.

Phalkey, R. and V. Louis, 2016: Two hot to handle: How do we manage the

simultaneous impacts of climate change and natural disasters on human

health? Eur. Phys. J. Special Top., 225(3), 443–457, doi:10.1140/epjst/e2016-

60071-y.

Phelan, A.L., R. Katz and L.O. Gostin, 2020: The novel coronavirus originating in

Wuhan, China challenges for global health governance. J. Am. Med. Assoc.,

323(8), 709–710, doi:10.1001/jama.2020.1097.

Philipsborn, R., S.M. Ahmed, B.J. Brosi and K. Levy, 2016: Climatic drivers of

Diarrheagenic Escherichia coli incidence: a systematic review and meta-

analysis. J. Infect. Dis., 214(1), 6–15, doi:10.1093/infdis/jiw081.

Phillips, C.A., etal., 2020: Compound climate risks in the COVID-19 pandemic.

Nat. Clim. Change, 10, 586–598.

Phung, D., et al., 2017: Heatwave and risk of hospitalization: a multi-

province study in Vietnam. Environ. Pollut., 220, 597–607, doi:10.1016/j.

envpol.2016.10.008.

Phung, D., et al., 2015: Climate change, water quality, and water-related

diseases in the Mekong Delta Basin: a systematic review. Asia Pac. J. Public

Health, 27(3), 265–276, doi:10.1177/1010539514565448.

Phung, D., et al., 2016: Ambient temperature and risk of cardiovascular

hospitalization: an updated systematic review and meta-analysis. Sci. Total

Environ., 550, 1084–1102, doi:10.1016/j.scitotenv.2016.01.154.

Piekałkiewicz, M., 2017: Why do economists study happiness? Econ. Labour

Relat. Rev., 28, 361–377, doi:10.1177/1035304617717130.

Piggott-McKellar, A., K. McNamara, P. Nunn and S. Sekinini, 2019: Moving

people in a changing climate: lessons from two case studies in Fiji. Soc. Sci.,

8, 133, doi:10.3390/socsci8050133.

Piguet, E., 2019: Climatic statelessness: risk assessment and policy options.

Popul. Dev. Rev., 45(4), 865–883, doi:10.1111/padr.12295.

Piguet, E., R. Kaenzig and J. Guélat, 2018: The uneven geography of research on

“environmental migration”. Popul. Environ., 39(4), 357–383, doi:10.1007/

s11111-018-0296-4.

Pineda, C., R. Munoz-Louis, C.V. Caballero-Uribe and D. Viasus, 2016:

Chikungunya in the region of the Americas. A challenge for rheumatologists

and health care systems. Clin. Rheumatol., 35(10), 2381–2385, doi:10.1007/

s10067-016-3390-y.

Piper, N., 2017: Migration and the SDGs. Glob. Soc. Policy, 17(2), 231–238.

Pisello, A.L., etal., 2018: Facing the urban overheating: recent developments.

Mitigation potential and sensitivity of the main technologies. Wiley

Interdiscip. Rev. Environ., 7(4), doi:10.1002/wene.294.

Poch, M., etal., 2020: When the fourth water and digital revolution encountered

COVID-19. Sci. Total Environ., 744, doi:10.1016/j.scitotenv.2020.140980.

Polain, J.D., H.L. Berry and J.O. Hoskin, 2011: Rapid change, climate adversity

and the next ‘big dry’: older farmers’ mental health. Aust. J. Rural Health,

19(5), 239–243, doi:10.1111/j.1440-1584.2011.01219.x.

Pollack, A., B. Weiss and T. Trung, 2016: Mental health, life functioning and risk

factors among people exposed to frequent natural disasters and chronic

poverty in Vietnam. BJPsych Open, 2(3), 221–232.

Pollard, G., M. Davies and H. Moore, 2015: Women, marketplaces and exchange

partners amongst the Marakwet of northwest Kenya. J. East. Afr. Stud., 9(3),

412–439, doi:10.1080/17531055.2015.1089699.

Polyakova, M., G. Kocks, V. Udalova and A. Finkelstein, 2020: Initial economic

damage from the COVID-19 pandemic in the United States is more

widespread across ages and geographies than initial mortality impacts. Proc.

Natl. Acad. Sci. U. S. A., doi:10.1073/pnas.2014279117.

Ponjoan, A., et al., 2017: Effects of extreme temperatures on cardiovascular

emergency hospitalizations in a Mediterranean region: a self-controlled case

series study. Environ. Health, 16, doi:10.1186/s12940-017-0238-0.

Poole, J.A., etal., 2019: Impact of weather and climate change with indoor

and outdoor air quality in asthmatic patients. Journal of Allergy and Clinical

Immunology, 143(5), 1702-1710

Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through

producers and consumers. Science, 360(6392), 987–992, doi:10.1126/

science.aag0216.

Poortinga, W., etal., 2019: Climate change perceptions and their individual-

level determinants: a cross-European analysis. Glob. Environ. Change, 55,

25–35.

Popov, I. and V. Yasyukevich, 2014: The taiga tick Ixodes persulcatus: propagation

under climate change conditions in the 21st century. Russ. Meteorol. Hydrol.,

39(8), 558–563, doi:10.3103/s1068373914080081.

Porst, L. and P. Sakdapolrak, 2020: Gendered translocal connectedness: rural–

urban migration, remittances, and social resilience in Thailand. Popul. Space

Place, 26(4), e2314, doi:10.1002/psp.2314.

Porter, C. and R. Goyal, 2016: Social protection for all ages? Impacts of

Ethiopia’s Productive Safety Net Program on child nutrition. Soc. Sci. Med.,

159, 92–99, doi:10.1016/j.socscimed.2016.05.001.

Portier, C., etal., 2010: A human health perspective on climate change. In:

Environmental Health Perspectives. National Institute of Environmental

Health Sciences, Research Triangle Park, NC.

Pottie, K., etal., 2015: Access to healthcare for the most vulnerable migrants: a

humanitarian crisis. Conﬂ. Health, 9, doi:10.1186/s13031-015-0043-8.

1159

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Power, M., S. Adar, J. Yanosky and J. Weuve, 2016: Exposure to air pollution

as a potential contributor to cognitive function, cognitive decline, brain

imaging, and dementia: a systematic review of epidemiologic research.

Neurotoxicology, 56, 235–253, doi:10.1016/j.neuro.2016.06.004.

Pozza, L.E. and D.J. Field, 2020: The science of soil security and food security.

Soil Security, DOI: https://doi.org/10.1016/j.soisec.2020.100002

Pramanik, M., etal., 2020: El Nino Southern Oscillation as an early warning

tool for dengue outbreak in India. BMC Public Health, 20(1), doi:10.1186/

s12889-020-09609-1.

Pretty, J. and J. Barton, 2020: Nature-based interventions and mind-body

interventions: saving public health costs whilst increasing life satisfaction

and happiness. Int. J. Environ. Res. Public Health, 17(21), doi:10.3390/

ijerph17217769.

Price, D., K.M. Hughes, F. Thien and C. Suphioglu, 2021: Epidemic thunderstorm

asthma: lessons learned from the storm down-under. J. Allergy Clin. Immunol.

Pract., 9(4), 1510–1515, doi:10.1016/j.jaip.2020.10.022.

Proestos, Y., etal., 2015: Present and future projections of habitat suitability

of the Asian tiger mosquito, a vector of viral pathogens, from global

climate simulation. Philos. Trans. Royal Soc. B, 370(1678), doi:10.1098/

rstb.2015.0246.

Prueksapanich, P., etal., 2018: Liver ﬂuke-associated biliary tract cancer. Gut

Liver, 12(3), 236–245, doi:10.5009/gnl17102.

Psarros, C., etal., 2017: Insomnia and PTSD one month after wildﬁres: evidence

for an independent role of the “fear of imminent death”. Int. J. Psychiat.

Clin., 21(2), 137–141, doi:10.1080/13651501.2016.1276192.

Pu, X., etal., 2017: Enhanced surface ozone during the heat wave of 2013

in Yangtze River Delta region, China. Sci. Total Environ., 603, 807–816,

doi:10.1016/j.scitotenv.2017.03.056.

Pyrgou, A., P. Hadjinicolaou and M. Santamouris, 2018: Enhanced near-surface

ozone under heatwave conditions in a Mediterranean island. Sci. Rep., 8,

doi:10.1038/s41598-018-27590-z.

Qiu, H., etal., 2016: Pneumonia hospitalization risk in the elderly attributable

to cold and hot temperatures in Hong Kong, China. Am. J. Epidemiol., 184(8),

570–578, doi:10.1093/aje/kww041.

Qu, J., 2016: Is sunspot activity a factor in inﬂuenza pandemics? Rev. Med.

Virol., 26(5), 309–313, doi:10.1002/rmv.1887.

Quast, T. and L. Feng, 2019: Long-term effects of disasters on health care

utilization: Hurricane Katrina and older individuals with diabetes. Disaster

Med. Public Health Prep., 1–8, doi:10.1017/dmp.2018.128.

Quintero-Herrera, L.L., etal., 2015: Potential impact of climatic variability on

the epidemiology of dengue in Risaralda, Colombia, 2010–2011. J. Infect.

Public Health, 8(3), 291–297, doi:10.1016/j.jiph.2014.11.005.

Ragettli, M.S. and M. Roosli, 2019: Heat-health action plans to prevent

heat-related deaths-experiences from Switzerland. Bundesgesundheitsbl.

Gesundheitsforsch. Gesundheitsschutz, 62(5), 605–611, doi:10.1007/

s00103-019-02928-8.

Raghavan, S.V., etal., 2019: ASEAN food security under the 2°C-4°C global

warming climate change scenarios. In: Disasters, Climate Change, and Food

Security: Supporting ASEAN Resilience [Anbum Ozhi, V., M.B. Reiling and V.

Reddy(eds.)], Towards a Resilient ASEAN, Vol. 1, Economic Research Institute

for ASEAN and East Asia, Jakarta.

Rahman, S., etal., 2019: Projected changes of inundation of cyclonic storms in

the Ganges–Brahmaputra–Meghna delta of Bangladesh due to SLR by 2100.

J. Earth Syst. Sci., doi:10.1007/s12040-019-1184-8.

Rail, M., etal., 2019: Impact of climate and population change on temperature-

related mortality burden in Bavaria, Germany. Environ. Res. Lett., 14(12),

doi:10.1088/1748-9326/ab5ca6.

Rakotoarison, N., etal., 2018: Assessment of risk, vulnerability and adaptation

to climate change by the health sector in Madagascar. Int. J. Environ. Res.

Public Health, 15(12), doi:10.3390/ijerph15122643.

Ramamurthy, P. and E. Bou-Zeid, 2017: Heatwaves and urban heat islands: a

comparative analysis of multiple cities. J. Geophys. Res., 122(1), 168–178,

doi:10.1002/2016jd025357.

Ramesh, A., etal., 2016: The impact of climate on the abundance of Musca

sorbens, the vector of trachoma. Parasit Vectors, 9, 48, doi:10.1186/s13071-

016-1330-y.

Ramos-Castillo, A., E. Castellanos and K. McLean, 2017: Indigenous peoples,

local communities and climate change mitigation. Clim. Change, 140(1),

1–4, doi:10.1007/s10584-016-1873-0.

Ramos, A.K., etal., 2020: Invisible no more: the impact of COVID-19 on essential

food production workers. J. Agromed., 25, 378–382.

Rankoana, S.A., 2021: Climate change impacts on indigenous health promotion:

the case study of Dikgale community in Limpopo Province, South Africa.

Glob. Health Promot., doi:10.1177/17579759211015183.

Ranson, M., 2014: Crime, weather, and climate change. J. Environ. Econ.

Manag., 67(3), 274–302, doi:10.1016/j.jeem.2013.11.008.

Rao, K.K., et al., 2020: Projections of heat stress and associated work

performance over India in response to global warming. Sci. Rep., 10(1),

doi:10.1038/s41598-020-73245-3.

Rao, M.R.K., R.N. Padhy and M.K. Das, 2018: Episodes of the epidemiological

factors correlated with prevailing viral infections with dengue virus

and molecular characterization of serotype-speciﬁc dengue virus

circulation in eastern India. Infect. Genet. Evol., 58, 40–49, doi:10.1016/j.

meegid.2017.12.005.

Rao, N., 2019: From abandonment to autonomy: gendered strategies for

coping with climate change, Isiolo County, Kenya. Geoforum, 102, 27–37,

doi:10.1016/j.geoforum.2019.03.017.

Rao, N., etal., 2019a: Gendered vulnerabilities to climate change: insights from

the semi-arid regions of Africa and Asia. Clim. Dev., 11(1), 14–26.

Rao, N., et al., 2019b: Managing risk, changing aspirations and household

dynamics: implications from wellbeing and adaptation in semi-arid Africa

and India. World Dev., doi:10.1016/j.worlddev.2019.104667.

Rataj, E., K. Kunzweiler and S. Garthus-Niegel, 2016: Extreme weather events

in developing countries and related injuries and mental health disorders – a

systematic review. BMC Public Health, 16(1), 1020, doi:10.1186/s12889-

016-3692-7.

Ravera, F., B. Martin-Lopez, U. Pascual and A. Drucker, 2016: The diversity of

gendered adaptation strategies to climate change of Indian farmers: a

feminist intersectional approach. Ambio, 45, 335–351, doi:10.1007/s13280-

016-0833-2.

Raymond, C., T. Matthews and R.M. Horton, 2020: The emergence of heat

and humidity too severe for human tolerance. Sci. Adv., 6(19), doi:10.1126/

sciadv.aaw1838.

Reckien, D., 2021: What can local climate planning learn from COVID-19?

Transform the city – it saves the climate and lowers the risk of pandemics.

Plan. Theory Pract., doi:10.1080/14649357.2021.1951578.

Regli, S., etal., 2015: Estimating potential increased bladder cancer risk due to

increased bromide concentrations in sources of disinfected drinking waters.

Environ. Sci. Technol., 49(22), 13094–13102, doi:10.1021/acs.est.5b03547.

Reid, C.E., et al., 2016: Critical review of health impacts of wildﬁre smoke

exposure. Environ. Health Perspect., 124(9), 1334–1343, doi:10.1289/

ehp.1409277.

Reid, P., D. Cormack and S.J. Paine, 2019: Colonial histories, racism and health

– the experience of Māori and indigenous peoples. Public Health, 172, 119–

124, doi:10.1080/10705422.2019.1658677.

Remling, E., 2020: Migration as climate adaptation? Exploring discourses

amongst development actors in the Paciﬁc Island region. Reg. Environ.

Change, 20(1), 3, doi:10.1007/s10113-020-01583-z.

Reser, J.P., etal., 2012: Public Risk Perceptions, Understandings, and Responses

to Climate Change and Natural Disasters in Australia, 2010 and 2011.

National Climate Change Adaptation Research Facility, Gold Coast. 245 pp.

Reyer, C.P., et al., 2017: Climate change impacts in Central Asia and their

implications for development. Reg. Environ. Change, 17(6), 1639–1650.

Reyes, M.E.S., et al., 2021: An investigation into the relationship between

climate change anxiety and mental health among Gen Z Filipinos. Curr.

Psychol., 1–9, doi:10.1007/s12144-021-02099-3.

1160

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Rice, M.B., G.D. Thurston, J.R. Balmes and K.E. Pinkerton, 2014: Climate change.

A global threat to cardiopulmonary health. Am. J. Respir. Crit. Care Med.,

189(5), 512–519, doi:10.1164/rccm.201310-1924PP.

Richmond, C.A.M. and N.A. Ross, 2009: The determinants of First Nation and

Inuit health: a critical population health approach. Health Place, 15(2), 403–

411, doi:10.1016/j.healthplace.2008.07.004.

Rickerts, V., 2019: Climate change and systemic fungal infections.

Bundesgesundheitsbl. Gesundheitsforsch. Gesundheitsschutz, 62(5), 646–

651, doi:10.1007/s00103-019-02931-z.

Rigaud, K.K., et al., 2018: Groundswell: Preparing for Internal Climate

Migration. Washington DC, https://openknowledge.worldbank.org/

handle/10986/29461. Last accessed 3 September 2021.

Rigg, J., T. Nguyen, T. Thu and H. Luong, 2018: The texture of livelihoods:

migration and making a living in Hanoi. J. Dev. Stud., 50, 368–382.

Rigg, J. and A. Salamanca, 2015: The devil in the details: interpreting livelihood

turbulence from a 25-year panel study from Thailand. Area, 47(3), 296–304.

Rigi, H. and J.F. Warner, 2020: Two-level games on the trans-boundary river

Indus: obstacles to cooperation. Water Policy, 22(6), 972–990.

Riley, M., 2021: The New South Wales air quality alert system: a brief history.

Aust. J. Emerg. Manag., 36(1), 21–24.

Riosmena, F., R. Kuhn and W.C. Jochem, 2017: Explaining the immigrant health

advantage: self-selection and protection in health-related factors among ﬁve

major national-origin immigrant groups in the United States. Demography,

54(1), 175–200, doi:10.1007/s13524-016-0542-2.

Riosmena, F., R. Nawrotzki and L. Hunter, 2018: Climate migration at the height

and end of the Great Mexican Emigration Era. Popul. Dev. Rev., 44(3), 455–

488, doi:10.1111/padr.12158.

Rippin, H., etal., 2020: Disrupted food systems in the WHO European region – a

threat or opportunity for healthy and sustainable food and nutrition? Food

Sec., 12(4), 859–864, doi:10.1007/s12571-020-01079-y.

Ripple, W., etal., 2014: Commentary: Ruminants, climate change and climate

policy. Nat. Clim. Change, 4(1), 2–5, doi:10.1038/nclimate2081.

Ritchie, H., D. Reay and P. Higgins, 2018: Beyond calories: a holistic assessment

of the global food system. Front. Sustain. Food Syst., 2, doi:10.3389/

fsufs.2018.00057.

Rivas, A.V., et al., 2018: Building Infestation Index for Aedes aegypti and

occurrence of dengue fever in the municipality of Foz do Iguacu, Parana,

Brazil, from 2001 to 2016. Rev. Soc. Bras. Med. Trop., 51(1), 71–76,

doi:10.1590/0037-8682-0228-2017.

Roa, L., etal., 2020: Climate change: challenges and opportunities to scale

up surgical, obstetric, and anaesthesia care globally. Lancet Planet. Health,

4(11), E538–E543.

Robalino, J., J. Jiminez and A. Chacón, 2015: The effect of hydro-meteorological

emergencies on internal migration. World Dev., 67, 438–448.

Roberts, I. and E. Arnold, 2007: Policy at the crossroads: climate change and

injury control. Inj. Prev., 13(4), 222–223.

Roberts, I. and M. Hillman, 2005: Climate change: the implications for policy on

injury control and health promotion. Inj. Prev., 11(6), 326–329.

Roche, K.R., etal., 2020: Climate change and the opportunity cost of conﬂict.

Proc. Natl. Acad. Sci., 117(4), 1935–1940.

Rocklöv, J. and R. Dubrow, 2020: Climate change: an enduring challenge for

vector-borne disease prevention and control. Nat. Immunol., 21(5), 479–483.

Rocklöv, J., V. Huber, K. Bowen and R. Paul, 2021: Taking globally consistent

health impact projections to the next level. Lancet Planet. Health. DOI:

https://doi.org/10.1016/S2542-5196(21)00171-6

Rocklov, J., etal., 2019: Using big data to monitor the introduction and spread

of Chikungunya, Europe, 2017. Emerg. Infect. Dis., 25(6), 1041–1049,

doi:10.3201/eid2506.180138.

Roda Gracia, J., B. Schumann and A. Seidler, 2015: Climate variability and the

occurrence of human Puumala hantavirus infections in Europe: a systematic

review. Zoonoses Public Health, 62(6), 465–478, doi:10.1111/zph.12175.

Rodrigues, M., P. Santana and A. Rocha, 2020: Statistical modelling of

temperature-attributable deaths in Portuguese metropolitan areas

under climate change: who is at risk? Atmosphere, 11(2), doi:10.3390/

atmos11020159.

Rodriguez-Llanes, J., S. Ranjan-Dash, A. Mukhopadhyay and D. Guha-Sapir,

2016: Looking upstream: enhancers of child nutritional status in post-ﬂood

rural settings. PeerJ, 4, doi:10.7717/peerj.1741.

Rogers, D.J., 2015: Dengue: recent past and future threats. Philos. Trans. R. Soc.

Lond. B Biol. Sci., 370(1665), doi:10.1098/rstb.2013.0562.

Rohat, G., etal., 2019a: Projections of human exposure to dangerous heat in

African cities under multiple socioeconomic and climate scenarios. Earths

Future, 7(5), 528–546, doi:10.1029/2018ef001020.

Rohat, G., etal., 2019b: Inﬂuence of changes in socioeconomic and climatic

conditions on future heat-related health challenges in Europe. Glob. Planet.

Change, 172, 45–59, doi:10.1016/j.gloplacha.2018.09.013.

Rohat, G., etal., 2019c: Characterizing the role of socioeconomic pathways

in shaping future urban heat-related challenges. Sci. Total Environ., 695,

doi:10.1016/j.scitotenv.2019.133941.

Rojas-Rueda, D., etal., 2016: Health impacts of active transportation in Europe.

PLoS ONE, 11(3), doi:10.1371/journal.pone.0149990.

Roos, N., etal., 2021: Maternal and newborn health risks of climate change: a

call for awareness and global action. Acta Obstet. Gynecol. Scand., 100(4),

566–570, doi:10.1111/aogs.14124.

Rosenberg, A., et al., 2018: Ambient temperature and age-related notiﬁed

Campylobacter infection in Israel: a 12-year time series study. Environ. Res.,

164, 539–545, doi:10.1016/j.envres.2018.03.017.

Rosenbloom, D. and J. Markard, 2020: A COVID-19 recovery for climate. Science,

368(6490), 447–447, doi:10.1126/science.abc4887.

Rosenstock, T., et al., 2019: A planetary health perspective on agroforestry

in Sub-Saharan Africa. One Earth, 1(3), 330–344, doi:10.1016/j.

oneear.2019.10.017.

Roth, D., et al., 2019: Climates of urbanization: local experiences of water

security, conﬂict and cooperation in peri-urban South-Asia. Clim. Policy,

19(sup1), S78–S93.

Roth, G.A., et al., 2020: Global burden of cardiovascular diseases and risk

factors, 1990–2019: update from the GBD 2019study. J. Am. Coll. Cardiol.,

76(25), 2982–3021, doi:10.1016/j.jacc.2020.11.010.

Rozenberg, H., 2017: Climate change through a poverty lens. Nat. Clim. Change,

7(4), 250–256.

Rüegger, S. and H. Bohnet, 2020: The link between forced migration and

conﬂict. In: Environmental Conﬂicts, Migration and Governance, Bristol

University Press, Bristol, pp. 177.

Rushton, S.P., etal., 2019: Climate, human behaviour or environment: individual-

based modelling of Campylobacter seasonality and strategies to reduce

disease burden. J. Transl. Med., 17(1), 34, doi:10.1186/s12967-019-1781-y.

Russo, S., A. F. Marchese, J. Sillmann and G. Imme, 2016: When will unusual

heat waves become normal in a warming Africa? Environ. Res. Lett., 11(5),

doi:10.1088/1748-9326/11/5/054016.

Russo, S., etal., 2019: Half a degree and rapid socioeconomic development

matter for heatwave risk. Nat. Commun., 10, doi:10.1038/s41467-018-

08070-4.

Ruszkiewicz, J., etal., 2019: Brain diseases in changing climate. Environ. Res.,

177, doi:10.1016/j.envres.2019.108637.

Rüttinger, L., etal., 2015: A New Climate for Peace: Taking Action on Climate

and Fragility Risks. Adelphi, Berlin/London/Washington/Paris.

Ruuhela, R., etal., 2021: Temperature-related mortality in Helsinki compared to

its surrounding region over two decades, with special emphasis on intensive

heatwaves. Atmosphere, 12(1), doi:10.3390/atmos12010046.

Ryan, B., etal., 2015a: Identifying and describing the impact of cyclone, storm and

ﬂood related disasters on treatment management, care and exacerbations

of non-communicable diseases and the implications for public health. PLoS

Curr., 7, doi:10.1371/currents.dis.62e9286d152de04799644dcca47d9288.

Ryan, S., C. Carlson, E. Mordecai and L. Johnson, 2019: Global expansion and

redistribution of Aedes-borne virus transmission risk with climate change.

PLoS Negl. Trop. Dis., 13(3), doi:10.1371/journal.pntd.0007213.

1161

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Ryan, S., C. Lippi and F. Zermoglio, 2020: Shifting transmission risk for malaria

in Africa with climate change: a framework for planning and intervention.

Malar. J., 19(1), doi:10.1186/s12936-020-03224-6.

Ryan, S.J., etal., 2015b: Mapping physiological suitability limits for malaria in

Africa under climate change. Vector Borne Zoonotic Dis., 15(12), 718–725,

doi:10.1089/vbz.2015.1822.

Sabate, J. and S. Soret, 2014: Sustainability of plant-based diets: back to the

future. Am. J. Clin. Nutr., 100, 476S–483S.

Saeed, F., C.F. Schleussner and M. Almazroui, 2021: From Paris to Makkah: heat

stress risks for Muslim pilgrims at 1.5degrees C and 2degrees C. Environ.

Res. Lett., 16(2), doi:10.1088/1748-9326/abd067.

Saha, S.K., 2016: Cyclone Aila, livelihood stress, and migration: empirical

evidence from coastal Bangladesh. Disasters, doi:10.1111/disa.12214.

Salas, R. and A. Jha, 2019: Climate change threatens the achievement of

effective universal healthcare. BMJ, 366, doi:10.1136/bmj.l5302.

Samir, K. and W. Lutz, 2017: The human core of the shared socioeconomic

pathways: population scenarios by age, sex and level of education for all

countries to 2100. Glob. Environ. Change Policy Dimens., 42, 181–192,

doi:10.1016/j.gloenvcha.2014.06.004.

Sampedro, J., etal., 2020: Health co-beneﬁts and mitigation costs as per the

Paris Agreement under different technological pathways for energy supply.

Environ. Int., 136, 1–12, doi:10.1016/j.envint.2020.105513.

Sangha, K.K., et al., 2018: An ecosystem services framework to evaluate

Indigenous and local peoples’ connections with nature. Ecosyst. Serv., 31,

111–125.

Santiago, E.C. and F. Rivas, 2012: Polychlorinated biphenyls in selected sites

in Pasig River and Laguna Lake in the Philippines before and after a big

ﬂood event investigated under the UNU East Asia Regional POPs monitoring

project. Bull. Environ. Contam. Toxicol., 89(2), 407–411, doi:10.1007/

s00128-012-0677-9.

Sanz-Barbero, B., etal., 2018: Heat wave and the risk of intimate partner violence.

Sci. Total Environ., 644, 413–419, doi:10.1016/j.scitotenv.2018.06.368.

Sapkota, A., et al., 2020: Association between changes in timing of spring

onset and asthma hospitalization in Maryland. JAMA Network Open, 3(7).

DOI:10.1001/jamanetworkopen.2020.7551

Sarkar, S., V. Gangare, P. Singh and R. Dhiman, 2019: Shift in potential malaria

transmission areas in India, using the fuzzy-based climate suitability malaria

transmission (FCSMT) model under changing climatic conditions. Int. J.

Environ. Res. Public Health, 16(18), doi:10.3390/ijerph16183474.

Sattler, D.N., et al., 2020: Climate change in Tonga: risk perception and

behavioral adaptation. In: Managing Climate Change Adaptation in the

Paciﬁc Region, Springer, Cham, pp. 273–292.

Sattler, D.N., A. Whippy, J.M. Graham and J. Johnson, 2018: A psychological

model of climate change adaptation: inﬂuence of resource loss, posttraumatic

growth, norms, and risk perception following Cyclone Winston in Fiji. In:

Climate Change Impacts and Adaptation Strategies for Coastal Communities,

Springer, Dordrecht, pp. 427–443.

Sawas, A., M. Workman and N. Mirumachi, 2018: Climate Change, Low-carbon

Transitions and Security. Grantham Institute Brieﬁng paper, Vol. 25. Imperial

College, London.

Scarpa, G., etal., 2021: Developing an online food composition database for an

Indigenous population in south-western Uganda. Public Health Nutr., 24(9),

2455–2464, doi:10.1017/s1368980021001397.

Scasny, M., E. Massetti, J. Melichar and S. Carrara, 2015: Quantifying the

ancillary beneﬁts of the representative concentration pathways on air

quality in Europe. Environ. Resour. Econ., 62(2), 383–415, doi:10.1007/

s10640-015-9969-y.

Schachtel, A., J.A. Dyer and M.D. Boos, 2021: Climate change and pediatric

skin health. Int. J. Womens Dermatol., 7(1), 85–90, doi:10.1016/j.

ijwd.2020.07.006.

Schaferhoff, M., et al., 2019: Trends in global health ﬁnancing. BMJ, 365,

doi:10.1136/bmj.l2185.

Scheelbeek, P., etal., 2021: The effects on public health of climate change

adaptation responses: a systematic review of evidence from low- and

middle-income countries. Environ. Res. Lett., 16(7), doi:10.1088/1748-9326/

ac092c.

Scheffran, J., etal., 2012: Disentangling the climate-conﬂict nexus: empirical

and theoretical assessment of vulnerabilities and pathways. Rev. Eur. Stud.,

4(5), 13.

Scheidel, A., et al., 2020: Environmental conﬂicts and defenders: a global

overview. Glob. Environ. Change, 63, 102104.

Schinasi, L.H., T. Benmarhnia and A.J. De Roos, 2018: Modiﬁcation of the

association between high ambient temperature and health by urban

microclimate indicators: a systematic review and meta-analysis. Environ.

Res., 161, 168–180, doi:10.1016/j.envres.2017.11.004.

Schipper, E., etal., 2021: Turbulent transformation: abrupt societal disruption

and climate resilient development. Clim. Dev., 13(6), 467–474.

Schipper, E.L.F., 2020: Maladaptation: when adaptation to climate change goes

very wrong. One Earth, 3(4), 409–414, doi:10.1016/j.oneear.2020.09.014.

Schleussner, C.-F., J.F. Donges, R.V. Donner and H.J. Schellnhuber, 2016:

Armed-conﬂict risks enhanced by climate-related disasters in ethnically

fractionalized countries. Proc. Natl. Acad. Sci., 113(33), 9216–9221.

Schneider, A., etal., 2020: Impact of meteorological parameters on suicide

mortality rates: a case-crossover analysis in Southern Germany (1990–2006).

Sci. Total Environ., 707, doi:10.1016/j.scitotenv.2019.136053.

Schnell, J.L. and M.J. Prather, 2017: Co-occurrence of extremes in surface ozone,

particulate matter, and temperature over eastern North America. Proc. Natl.

Acad. Sci. U.S.A., 114(11), 2854–2859, doi:10.1073/pnas.1614453114.

Schramm, P., etal., 2020a: Climate change and health: local solutions to local

challenges. Curr. Environ. Health Rep., 7(4), 363–370, doi:10.1007/s40572-

020-00294-1.

Schramm, P., et al., 2020b: How indigenous communities are adapting to

climate change: insights from the climate-ready tribes initiative. Health Aff.,

39(12), 2153–2159, doi:10.1377/hlthaff.2020.00997.

Schucht, S., etal., 2015: Moving towards ambitious climate policies: Monetised

health beneﬁts from improved air quality could offset mitigation costs in

Europe. Environ. Sci. Policy, 50, 252–269, doi:10.1016/j.envsci.2015.03.001.

Schumacher, K. A. and V. Shandas, 2019: Rescaling air quality management: an

assessment of local air quality authorities in the United States. Air Soil Water

Res., 12, doi:10.1177/1178622119842125.

Schuster, C., J. Honold, S. Lauf and T. Lakes, 2017: Urban heat stress: novel survey

suggests health and ﬁtness as future avenue for research and adaptation

strategies. Environ. Res. Lett., 12, doi:10.1088/1748-9326/aa5f35.

Schutte, S., J. Vestby, J. Carling and H. Buhaug, 2021: Climatic conditions

are weak predictors of asylum migration. Nat. Commun., 12(1), 2067,

doi:10.1038/s41467-021-22255-4.

Schwartz, R., etal., 2015: The impact of Hurricane Sandy on the mental health

of New York area residents. Am. J. Disaster Med., 10(4), 339–346.

Schwarz, L., etal., 2021: Spatial variation in the joint effect of extreme heat

events and ozone on respiratory hospitalizations in California. Proc. Natl.

Acad. Sci. U.S.A., 118(22), doi:10.1073/pnas.2023078118.

Schweitzer, M.D., etal., 2018: Lung health in era of climate change and dust

storms. Environ. Res., 163, 36–42, doi:10.1016/j.envres.2018.02.001.

Schwerdtle, P., K. Bowen and C. McMichael, 2018: The health impacts of

climate-related migration. BMC Med., 16(1), 1–7.

Schwerdtle, P., etal., 2020: Health and migration in the context of a changing

climate: a systematic literature assessment. Environ. Res. Lett., 15(10),

doi:10.1088/1748-9326/ab9ece.

Schwingshackl, C., et al., 2021: Heat stress indicators in CMIP6: estimating

future trends and exceedances of impact-relevant thresholds. Earths Future,

9(3), doi:10.1029/2020ef001885.

Scortichini, M., etal., 2018: The inter-annual variability of heat-related mortality

in nine European cities (1990–2010). Environ. Health, 17(1), 66, doi:10.1186/

s12940-018-0411-0.

1162

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Scott, A.A., D.W. Waugh and B. Zaitchik, 2018: Reduced Urban Heat

Island intensity under warmer conditions. Environ. Res. Lett., 13(6),

doi:10.1088/1748-9326/aabd6c.

Scott, C.A., etal., 2019: Water in the Hindu Kush Himalaya. In: The Hindu Kush

Himalaya Assessment. Springer, Berlin Heidelberg, pp. 257–299.

Selby, J., 2019: Climate change and the Syrian civil war, Part II: the Jazira’s

agrarian crisis. Geoforum, 101, 260–274.

Selby, J., O.S. Dahi, C. Frohlich and M. Hulme, 2017: Climate change and

the Syrian civil war revisited. Polit. Geogr., 60, 232–244, doi:10.1016/j.

polgeo.2017.05.007.

Sellers, S. and K.L. Ebi, 2017: Climate change and health under the shared

socioeconomic pathway framework. Int. J. Environ. Res. Public Health, 15(1),

doi:10.3390/ijerph15010003.

Semakula, H., et al., 2017: Prediction of future malaria hotspots under

climate change in sub-Saharan Africa. Clim. Change, 143(3-4), 415–428,

doi:10.1007/s10584-017-1996-y.

Semenza, J., 2020: Cascading risks of waterborne diseases from climate change.

Nat. Immunol., 21(5), 484–487, doi:10.1038/s41590-020-0631-7.

Semenza, J., 2021: Lateral public health: advancing systemic resilience

to climate change. Lancet Reg. Health. DOI:https://doi.org/10.1016/j.

lanepe.2021.100231

Semenza, J., etal., 2021: COVID-19 research priorities for non-pharmaceutical

public health and social measures. Epidemiol. Infect., 149, e87, doi:10.1017/

s0950268821000716.

Semenza, J., et al., 2016: Determinants and drivers of infectious disease

threat events in Europe. Emerg. Infect. Dis., 22(4), 581–589, doi:10.3201/

eid2204.151073.

Semenza, J., et al., 2017: Environmental suitability of vibrio infections in

a warming climate: an early warning system. Environ. Health Perspect.,

125(10), doi:10.1289/ehp2198.

Semenza, J. and H. Zeller, 2014: Integrated surveillance for prevention and

control of emerging vector-borne diseases in Europe. Euro Surveill., 19(13),

2–5, doi:10.2807/1560-7917.Es2014.19.13.20757.

Semenza, J.C., 2011: Lateral public health: a comprehensive approach to

adaptation in urban environments. In: Climate Change Adaptation in

Developed Nations: From Theory to Practice Advances in Global Research

[Ford, J.D. and L. Berrang-Ford(eds.)]. Springer Science +Business Media,

New York, NY, pp. 143–159.

Semenza, J.C. and K.L. Ebi, 2019: Climate change impact on migration,

travel, travel destinations and the tourism industry. J. Travel Med., 26(5),

doi:10.1093/jtm/taz026.

Semenza, J.C. and S. Paz, 2021: Climate change and infectious disease in

Europe: impact, projection and adaptation. Lancet Reg. Health. https://doi.

org/10.1016/j.lanepe.2021.100230

Sen, A., 2001: Development as Freedom. Oxford University Press, Oxford.

Sera, F., etal., 2019: How urban characteristics affect vulnerability to heat

and cold: a multi-country analysis. Int. J. Epidemiol., 48(4), 1101–1112,

doi:10.1093/ije/dyz008.

Sera, F., et al., 2020: Air conditioning and heat-related mortality A multi-

country longitudinal study. Epidemiology, 31(6), 779–787, doi:10.1097/

ede.0000000000001241.

Serdeczny, O., etal., 2017: Climate change impacts in Sub-Saharan Africa: from

physical changes to their social repercussions. Reg. Environ. Change, 17(6),

1585–1600, doi:10.1007/s10113-015-0910-2.

Shaffer, R., et al., 2019: Improving and expanding estimates of the global

burden of disease due to environmental health risk factors. Environ. Health

Perspect., 127(10), doi:10.1289/ehp5496.

Shafﬁe, A., 2021: International institutions and global governance on climate

change.Chapter 20. In: Global Climate Change and Human Health: from

Science to Practice [Lemery, J., K. Knowlton and C. Sorensen(eds.)]. John

Wiley & Sons, Hoboken NJ.

Shair-Rosenﬁeld, S. and R.M. Wood, 2017: Governing well after war: how

improving female representation prolongs post-conﬂict peace. J. Polit.,

79(3), 995–1009.

Shaman, J. and M. Lipsitch, 2013: The El Nino-Southern Oscillation (ENSO)-

pandemic Inﬂuenza connection: coincident or causal? Proc. Natl. Acad. Sci.

U.S.A., 110, 3689–3691, doi:10.1073/pnas.1107485109.

Shayegh, S., 2017: Outward migration may alter population dynamics and

income inequality. Nat. Clim. Change, 7, 828–828.

Shekhar, M., etal., 2018: Effects of climate change on occurrence of aﬂatoxin

and its impacts on maize in India. Int. J. Curr. Microbiol. App. Sci, 7(6), 109–

116.

Sheller, M., 2018: Theorising mobility justice. Tempo Soc., 30(2), 17–34,

doi:10.11606/0103-2070.ts.2018.142763.

Sheng, R., etal., 2018: Does hot weather affect work-related injury? A case-

crossover study in Guangzhou, China. Int. J. Hyg. Environ. Health, 221(3),

423–428, doi:10.1016/j.ijheh.2018.01.005.

Sheridan, S. and M. Allen, 2018: Temporal trends in human vulnerability to

excessive heat. Environ. Res. Lett., 13, doi:10.1088/1748-9326/aab214.

Sherpa, A., T. Koottatep, C. Zurbrugg and G. Cisse, 2014: Vulnerability and

adaptability of sanitation systems to climate change. J. Water Clim. Change,

5(4), 487–495, doi:10.2166/wcc.2014.003.

Shi, J., Z.Y. Chen, S.S. Ding and Y.Q. Lu, 2020: A hot blob eastward of New Zealand

in December 2019. Atmosphere, 11(12), doi:10.3390/atmos11121267.

Shih, D.H., T.W. Wu, W.X. Liu and P.Y. Shih, 2019: An Azure ACES early warning

system for air quality index deteriorating. Int. J. Environ. Res. Public Health,

16(23), doi:10.3390/ijerph16234679.

Shindell, D., G. Faluvegi, K. Seltzer and C. Shindell, 2018: Quantiﬁed, localized

health beneﬁts of accelerated carbon dioxide emissions reductions. Nat.

Clim. Change, 8(4), 291, doi:10.1038/s41558-018-0108-y.

Shooshtarian, S., P. Rajagopalan and A. Sagoo, 2018: A comprehensive review

of thermal adaptive strategies in outdoor spaces. Sustain. Cities Soc., 41,

647–665, doi:10.1016/j.scs.2018.06.005.

Shrestha, N., etal., 2020: The impact of COVID-19 on globalization. One Health,

100180, doi:10.1016/j.onehlt.2020.100180.

Shultz, J., C. Fugate and S. Galea, 2020: Cascading risks of COVID-19 resurgence

during an active 2020 Atlantic hurricane season. JAMA, 324(10), 935–936,

doi:10.1001/jama.2020.15398.

Shultz, J.M., etal., 2018a: Risks, health consequences, and response challenges

for small-island-based populations: observations from the 2017 Atlantic

hurricane season. Disaster Med. Public Health Prep., 1–13, doi:10.1017/

dmp.2018.28.

Shultz, J.M., J. Russell and Z. Espinel, 2005: Epidemiology of tropical cyclones:

the dynamics of disaster, disease, and development. Epidemiol. Rev., 27,

21–35, doi:10.1093/epirev/mxi011.

Shultz, J.M., et al., 2018b: Mitigating tropical cyclone risks and health

consequences: urgencies and innovations. Lancet Planet Health, 2(3), e103–

e104, doi:10.1016/s2542-5196(18)30021-4.

Shumake-Guillemot, J, etal., 2020: Technical Brief: Protecting Health from Hot

Weather During the COVID-19 Pandemic. Global Heat Health Information

Network, Geneva, https://ghhin.org/wp-content/uploads/technical-brief-

COVID-and-Heat-ﬁnalv2-1.pdf. Accessed 2 September 2021 . (8 pp).

Siddiqi, A., 2019: In the Wake of Disaster: Islamists, the State and a Social

Contract in Pakistan. Cambridge University Press, Cambridge, ISBN 978-

1108597708.

Siders, A.R., I. Ajibade and D. Casagrande, 2021: Transformative potential of

managed retreat as climate adaptation. Curr. Opin. Environ. Sustain., 50,

272–280. Available at: https://www.sciencedirect.com/science/article/pii/

S1877343521000798.

Sijbrandij, M., et al., 2020: The effect of psychological ﬁrst aid training on

knowledge and understanding about psychosocial support principles: a

cluster-randomized controlled trial. Int. J. Environ. Res. Public Health, 17(2),

doi:10.3390/ijerph17020484.

1163

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Silva, F.D., A.M. dos Santos, G. Correa Rda and J. Caldas Ade, 2016a: Temporal

relationship between rainfall, temperature and occurrence of dengue

cases in Sao Luis, Maranhao, Brazil. Cien. Saude Colet., 21(2), 641–646,

doi:10.1590/1413-81232015212.09592015.

Silva, M., A. Loureiro and G. Cardoso, 2016b: Social determinants of mental

health: a review of the evidence. Eur. J. Psychiatry, 30(4), 259–292.

Silva, R.A., etal., 2016c: The effect of future ambient air pollution on human

premature mortality to 2100 using output from the ACCMIP model ensemble.

Atmos. Chem. Phys., 16(15), 9847–9862, doi:10.5194/acp-16-9847-2016.

Silva, R.A., etal., 2017: Future global mortality from changes in air pollution

attributable to climate change. Nat. Clim. Change, 7(9), 647–651,

doi:10.1038/nclimate3354.

Silveira, I.H., T.R. Cortes, B.F.A. de Oliveira and W.L. Junger, 2021a: Projections of

excess cardiovascular mortality related to temperature under different climate

change scenarios and regionalized climate model simulations in Brazilian

cities. Environ. Res., 197, 110995, doi:10.1016/j.envres.2021.110995.

Silveira, S., etal., 2021b: Chronic mental health sequelae of climate change

extremes: a case study of the deadliest Californian wildﬁre. Int. J. Environ.

Res. Public Health, 18(4), doi:10.3390/ijerph18041487.

Singh, C., 2019: Migration as a driver of changing household structures:

implications for local livelihoods and adaptation. Migr. Dev., 8(3), 301–319,

doi:10.1080/21632324.2019.1589073.

Singh, C. and R. Basu, 2020: Moving in and out of vulnerability: Interrogating

migration as an adaptation strategy along a rural-urban continuum in India.

Geogr. J., 186(1), doi:10.1111/geoj.12328.

Singh, P.K. and H. Chudasama, 2021: Pathways for climate resilient development:

human well-being within a safe and just space in the 21st century. Glob.

Environ. Change, 68, 102277.

Sioui, M., 2019: Drought in the Yucatan: Maya perspectives on tradition, change,

and adaptation. In: Drought Challenges: Policy Options for Developing

Countries [Mapedza, E., D. Tsegai, M. Bruntrup and R. McLeman(eds.)].

Elsevier, Amsterdam.

Siraj, A., et al., 2014: Altitudinal changes in malaria incidence in highlands

of Ethiopia and Colombia. Science, 343(6175), 1154–1158, doi:10.1126/

science.1244325.

Smith, A., etal., 2016: Health and environmental co-beneﬁts and conﬂicts of

actions to meet UK carbon targets. Clim. Policy, 16(3), 253–283, doi:10.108

0/14693062.2014.980212.

Smith, B.A. and A. Fazil, 2019: How will climate change impact microbial

foodborne disease in Canada? Can. Commun. Dis. Rep., 45(4), 108–113,

doi:10.14745/ccdr.v45i04a05.

Smith, C., K. Dupre, A. McEvoy and S. Kenny, 2021: Community perceptions and

pro-environmental behavior: the mediating roles of social norms and climate

change risk. Can. J. Behav. Sci., 53(2), 200–210, doi:10.1037/cbs0000229.

Smith, J., etal., 2017: Malaria early warning tool: linking inter-annual climate

and malaria variability in northern Guadalcanal, Solomon Islands. Malar. J.,

16(1), 472, doi:10.1186/s12936-017-2120-5.

Smith, K., etal., 2014: Human health: impacts, adaptation, and co-beneﬁts. In:

Climate Change 2014: Impacts, Adaptation, and Vulnerability, Pt a: Global

and Sectoral Aspects, Cambridge University Press, Cambridge, UK, pp. 709–

754.

Smith, M.R. and S.S. Myers, 2018: Impact of anthropogenic CO2 emissions on

global human nutrition. Nat. Clim. Change, 8(9), 834–839, doi:10.1038/

s41558-018-0253-3.

Soebiyanto, R.P., etal., 2015: Associations between meteorological parameters

and inﬂuenza activity in Berlin (Germany), Ljubljana (Slovenia), Castile and

Leon (Spain) and Israeli districts. PLoS ONE, 10(8), doi:10.1371/journal.

pone.0134701.

Sohn, S., etal., 2019: ‘Pneumonia weather’: short-term effects of meteorological

factors on emergency room visits due to pneumonia in Seoul, Korea. J. Prev.

Med. Public. Health, 52(2), 82–91, doi:10.3961/jpmph.18.232.

Song, G., etal., 2017: Dietary changes to mitigate climate change and beneﬁt

public health in China. Sci. Total Environ., 577, 289–298, doi:10.1016/j.

scitotenv.2016.10.184.

Sorensen, C., etal., 2018: Climate change and women’s health: impacts and

opportunities in India. GeoHealth, 2(10) 283-297.

Sorgho, R., etal., 2020: Climate change policies in 16 West African countries: a

systematic review of adaptation with a focus on agriculture, food security,

and nutrition. Int. J. Environ. Res. Public Health, 17(23), doi:10.3390/

ijerph17238897.

Sovacool, B., D. Del Rio and S. Grifﬁths, 2020: Contextualizing the Covid-19

pandemic for a carbon-constrained world: insights for sustainability

transitions, energy justice, and research methodology. Energy Res. Soc. Sci.,

68, doi:10.1016/j.erss.2020.101701.

Sovacool, B.K., 2018: Bamboo beating Bandits: conﬂict, inequality, and

vulnerability in the political ecology of climate change adaptation in

Bangladesh. World Dev., 102, 183–194, doi:10.1016/j.worlddev.2017.10.014.

Sovacool, K., B. LinnÈr and M. Goodsite, 2015: The political economy of climate

adaptation. Nat. Clim. Change, 5, 616–618.

Sow, P., E. Marmer and J. Scheffran, 2016: Between the heat and the hardships.

Climate change and mixed migration ﬂows in Morocco. Migr. Dev., 5(2),

293–313.

Speakman, J.R., 2018: Obesity and thermoregulation. In: Thermoregulation:

from Basic Neuroscience to Clinical Neurology, Pt I, Vol. 156(431–443).

Elsevier, Amsterdam. doi:10.1016/b978-0-444-63912-7.00026-6.

Spearing, L.A. and K. M. Faust, 2020: Cascading system impacts of the 2018

Camp Fire in California: the interdependent provision of infrastructure

services to displaced populations. Int. J. Disaster Risk Reduct., 50, 101822,

doi:10.1016/j.ijdrr.2020.101822.

Spencer, N. and M. A. Urquhart, 2018: Hurricane strikes and migration: evidence

from storms in Central America and the Caribbean. Weather Clim. Soc.,

10(3), 569–577, doi:10.1175/wcas-d-17-0057.1.

Spentzou, E., M.J. Cook and S. Emmitt, 2021: Low-energy cooling and

ventilation refurbishments for buildings in a Mediterranean climate. Archit.

Eng. Des. Manag., doi:10.1080/17452007.2021.1926898.

Spilker, G., Q. Nguyen, V. Koubi and T. Bohmelt, 2020: Attitudes of urban

residents towards environmental migration in Kenya and Vietnam. Nat. Clim.

Change, 10(7), 622–627, doi:10.1038/s41558-020-0805-1.

Springmann, M., etal., 2018a: Options for keeping the food system within

environmental limits. Nature, 562(7728), 519–525, doi:10.1038/s41586-

018-0594-0.

Springmann, M., H.C.J. Godfray, M. Rayner and P. Scarborough, 2016a: Analysis

and valuation of the health and climate change cobeneﬁts of dietary change.

JProc. Natl. Acad. Sci., 113(15), 4146–4151, doi:10.1073/pnas.1523119113.

Springmann, M., etal., 2018b: Health-motivated taxes on red and processed

meat: a modelling study on optimal tax levels and associated health impacts.

PLoS ONE, 13(11), doi:10.1371/journal.pone.0204139.

Springmann, M., etal., 2016b: Global and regional health effects of future food

production under climate change: a modelling study. Lancet, 387(10031),

1937–1946, doi:10.1016/s0140-6736(15)01156-3.

Springmann, M., etal., 2020: The healthiness and sustainability of national

and global food based dietary guidelines: modelling study. BMJ, 370,

doi:10.1136/bmj.m2322.

Springmann, M., etal., 2018c: Health and nutritional aspects of sustainable

diet strategies and their association with environmental impacts: a global

modelling analysis with country-level detail. Lancet Planet. Health, 2(10),

e451–e461, doi:10.1016/s2542-5196(18)30206-7.

Squire, S.A. and U. Ryan, 2017: Cryptosporidium and Giardia in Africa: current

and future challenges. Parasit Vectors, 10(1), 195, doi:10.1186/s13071-017-

2111-y.

Staddon, C., etal., 2020: Water insecurity compounds the global coronavirus

crisis. Water Int., 45(5), 416–422, doi:10.1080/02508060.2020.1769345.

1164

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Steentjes, K., et al., 2017: European Perceptions of Climate Change: Topline

Findings of a Survey Conducted in Four European Countries in 2016. Cardiff

University, Cardiff.

Steffen, W., etal., 2015: Planetary boundaries: guiding human development on

a changing planet. Science, 347(6223), doi:10.1126/science.1259855.

Stensgaard, A., etal., 2013: Large-scale determinants of intestinal schistosomiasis

and intermediate host snail distribution across Africa: does climate matter?

Acta Trop., 128(2), 378–390, doi:10.1016/j.actatropica.2011.11.010.

Stevens, S.E., et al., 2019: Precipitation and fatal motor vehicle crashes:

continental analysis with high-resolution radar data. Bulletin of the

American Meteorological Society, 100(8), 1453-1461.

Stewart-Sinclair, P., etal., 2020: Blue restoration – building conﬁdence and

overcoming barriers. Front. Mar. Sci., 7, doi:10.3389/fmars.2020.541700.

Stewart, S., A.K. Keates, A. Redfern and J.J.V. McMurray, 2017: Seasonal

variations in cardiovascular disease. Nat. Rev. Cardiol., 14(11), 654–664,

doi:10.1038/nrcardio.2017.76.

Stojanov, R., B. Duži, I. Kelman, D. Němec, D. Procházka, 2016: Local perceptions

of climate change impacts and migration patterns in Malé, Maldives. Geogr.

J., 183(4), 370–385.

Stork, A., C. Travis and S. Halle, 2015: Gender-sensitivity in natural resource

management in Côte d’Ivoire and Sudan. Peace Rev., 27(2), 147–155, doi:1

0.1080/10402659.2015.1037617.

Subiros, M., etal., 2019: Health monitoring during water scarcity in Mayotte,

France, 2017. BMC Public Health, 19(1), 288, doi:10.1186/s12889-019-

6613-8.

Suckall, N., E. Fraser and P. Forster, 2017: Reduced migration under climate change:

evidence from Malawi using an aspirations and capabilities framework. Clim.

Dev., 9(4), 298–312, doi:10.1080/17565529.2016.1149441.

Suk, J.E., E.C. Vaughan, R.G. Cook and J.C. Semenza, 2020: Natural disasters

and infectious disease in Europe: a literature review to identify cascading

risk pathways. Eur. J. Public Health, 30(5), 928–935, doi:10.1093/eurpub/

ckz111.

Suliman, S., et al., 2019: Indigenous (im)mobilities in the Anthropocene.

Mobilities, 14(3), 298–318.

Sulser, T.B., etal., 2021: Disability-adjusted life years due to chronic and hidden

hunger under food system evolution with climate change and adaptation to

2050. Am. J. Clin. Nutr., 114(2), 550–563, doi:10.1093/ajcn/nqab101.

Sultana, P., et al., 2019: Transforming local natural resource conﬂicts to

cooperation in a changing climate: Bangladesh and Nepal lessons. Clim.

Policy, 19(sup1), S94–S106.

Sun, S., et al., 2019: Ambient temperature and markers of fetal growth:

a retrospective observational study of 29 million US singleton births.

Environmental health perspectives, 127(6).DOI: https://doi.org/10.1289/

EHP4648

Sun, Z., C. Chen, D. Xu and T. Li, 2018: Effects of ambient temperature on

myocardial infarction: a systematic review and meta-analysis. Environ.

Pollut., 241, 1106–1114, doi:10.1016/j.envpol.2018.06.045.

Swinburn, B.A., etal., 2019: The global syndemic of obesity, undernutrition,

and climate change: the Lancet commission report. Lancet, 393(10173),

791–846, doi:10.1016/s0140-6736(18)32822-8.

Sylla, M.B., etal., 2018: Projected heat stress under 1.5degrees C and 2degrees

C global warming scenarios creates unprecedented discomfort for humans

in West Africa. Earths Future, 6(7), 1029–1044, doi:10.1029/2018ef000873.

Tadgell, A., B. Doberstein and L. Mortsch, 2018: Principles for climate-related

resettlement of informal settlements in less developed nations: a review of

resettlement literature and institutional guidelines. Clim. Dev., 10(2), 102–

115, doi:10.1080/17565529.2017.1291401.

Tadgell, A., M. Linda and D. Brent, 2017: Assessing the feasibility of resettlement

as a climate change adaptation strategy for informal settlements in Metro

Manila, Philippines. Int. J. Disaster Risk Reduct., 22, 447–457, doi:10.1016/j.

ijdrr.2017.01.005.

Tainio, M., et al., 2017: Mortality, greenhouse gas emissions and consumer

cost impacts of combined diet and physical activity scenarios: a health

impact assessment study. BMJ Open, 7(2), e14199, doi:10.1136/

bmjopen-2016-014199.

Takebayashi, H., 2018: A simple method to evaluate adaptation measures for

urban heat island. Environments, 5(6), doi:10.3390/environments5060070.

Talhaoui, A., et al., 2019: Association between malnutrition and cognitive

impairment among Morocco’s older adults. Open J. Med. Psychol., 8(1), 1–14.

Talukder, M.R.R., etal., 2017: Drinking water salinity and risk of hypertension:

a systematic review and meta-analysis. Arch. Environ. Occup. Health, 72(3),

126–138, doi:10.1080/19338244.2016.1175413.

Tamerius, J.D., et al., 2013: Environmental predictors of seasonal inﬂuenza

epidemics across temperate and tropical climates. PLoS Pathog., 9(3),

doi:10.1371/journal.ppat.1003194.

Tamnes, R. and K. Offerdal, 2014: Geopolitics and Security in the Arctic: Regional

Dynamics in a Global World. Routledge, New York, ISBN 978-1317801597.

Tänzler, D., N. Scherer and Adelphi, 2019: Guidelines for Conﬂict-sensitive

Adaptation to Climate Change. Agency, G. E., Dessau-Roßlau.

Tayimlong, R.A., 2020: Fragility and insurgency as outcomes of

underdevelopment of public infrastructure and socio-economic deprivation:

the case of Boko Haram in the Lake Chad Basin. J. Peacebuild. Dev. 16(2),

209-223.

Taylor, D., etal., 2016a: Environmental change and Rift Valley fever in eastern

Africa: projecting beyond HEALTHY FUTURES. Geospat. Health, 11(1 Suppl),

387, doi:10.4081/gh.2016.387.

Taylor, L., et al., 2016b: The impact of different environmental conditions

on cognitive function: a focused review. Front. Physiol., 6, doi:10.3389/

fphys.2015.00372.

Taylor, R.G., etal., 2012: Ground water and climate change. Nat. Clim. Change,

3, 322, doi:10.1038/nclimate1744.

Taylor, S., 2020: Anxiety disorders, climate change, and the challenges ahead:

introduction to the special issue. J Anxiety Disord., 76.DOI: https://doi.

org/10.1016/j.janxdis.2020.102313

Teixidor-Toneu, I., etal., 2021: Which plants matter? A comparison of academic

and community assessments of plant value and conservation status in the

Moroccan High Atlas. Ambio, doi:10.1007/s13280-021-01584-0.

Telford, A., 2018: A threat to climate-secure European futures? Exploring

racial logics and climate-induced migration in US and EU climate security

discourses. Geoforum, 96, 268–277.

Tennison, I., etal., 2021: Health care’s response to climate change: a carbon

footprint assessment of the NHS in England. Lancet Planet. Health, 5(2),

E84–E92.

Terekhina, A., et al., 2021: The context of an emerging predation problem:

Nenets reindeer herders and Arctic foxes in Yamal. Eur. J. Wildl. Res., 67(3),

13, doi:10.1007/s10344-021-01497-z.

Tesla, B., et al., 2018: Temperature drives Zika virus transmission: evidence

from empirical and mathematical models. Proc. Royal Soc.B, 285(1884),

doi:10.1098/rspb.2018.0795.

Thaker, J., etal., 2015: The role of collective efﬁcacy in climate change adaptation

in India. Weather Clim. Soc., 8(1), 21–34, doi:10.1175/WCAS-D-14-00037.1.

Theisen, O.M., 2017: Climate change and violence: insights from political

science. Curr. Clim. Change Rep., 3(4), 210–221.

Thema, J., et al., 2017: More than energy savings: quantifying the multiple

impacts of energy efﬁciency in Europe. ECEEE 2017 Summer Study

conerence proceedings, 29May-03 June 2017, Presqu’ile de Giens, France,

pp.1727–1736.

Theobald, S., et al., 2018: Implementation research: new imperatives and

opportunities in global health. Lancet, 392(10160), 2214–2228, doi:10.1016/

S0140-6736(18)32205-0.

Thiam, S., et al., 2017: Association between childhood diarrhoeal incidence

and climatic factors in urban and rural settings in the health district of

Mbour, Senegal. Int. J. Environ. Res. Public Health, 14(9), doi:10.3390/

ijerph14091049.

Thiede, B. and C. Gray, 2020: Climate exposures and child undernutrition: evidence

from Indonesia. Soc. Sci. Med., 265, doi:10.1016/j.socscimed.2020.113298.

1165

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Thiery, W., etal., 2021: Age-dependent extreme event exposure. Science. Accepted

1 September 2021. https://spiral.imperial.ac.uk/bitstream/10044/1/92988/2/

manuscript.pdf

Thomas, A., etal., 2020: Climate change and small island developing states.

Annu. Rev. Environ. Resour., 45, 6.1–6.27.

Thomas, A. and L. Benjamin, 2020: Non-economic loss and damage: lessons

from displacement in the Caribbean. Clim. Policy, 20(6), 715–728, doi:10.10

80/14693062.2019.1640105.

Thompson, R., R. Hornigold, L. Page and T. Waite, 2018: Associations

between high ambient temperatures and heat waves with mental health

outcomes: a systematic review. Public Health, 161, 171–191, doi:10.1016/j.

puhe.2018.06.008.

Thornton, R.L., etal., 2016: Evaluating strategies for reducing health disparities

by addressing the social determinants of health. Health Aff., 35(8), 1416–

1423.

Tian, H.Y., etal., 2016: Surface water areas signiﬁcantly impacted 2014 dengue

outbreaks in Guangzhou, China. Environ. Res., 150, 299–305, doi:10.1016/j.

envres.2016.05.039.

Tian, Y., et al., 2019: Association between temperature variability and daily

hospital admissions for cause-speciﬁc cardiovascular disease in urban China:

a national time-series study. PLoS Med., 16, e1002738, doi:10.1371/journal.

pmed.1002738.

Tigchelaar, M., D.S. Battisti and J.T. Spector, 2020: Work adaptations insufﬁcient

to address growing heat risk for US agricultural workers. Environ. Res. Lett.,

15(9), doi:10.1088/1748-9326/ab86 f4.

Tiihonen, J., etal., 2017: The association of ambient temperature and violent

crime. Sci. Rep., 7(1), 1–7, doi:10.1038/s41598-017-06720-z.

Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability

and human health. Nature, 515(7528), 518–522, doi:10.1038/nature13959.

http://www.nature.com/nature/journal/v515/n7528/abs/nature13959.

html#supplementary-information.

Timler, K. and D. Sandy, 2020: Gardening in ashes: the possibilities and

limitations of gardening to support indigenous health and well-being in the

context of wildﬁres and colonialism. Int. J. Environ. Res. Public Health, 17(9),

doi:10.3390/ijerph17093273.

Timlin, U., etal., 2021: Living conditions and mental wellness in a changing

climate and environment: focus on community voices and perceived

environmental and adaptation factors in Greenland. Heliyon, 7(4), e6862,

doi:10.1016/j.heliyon.2021.e06862.

Timmerman, J., 2020: Building resilience through transboundary water

resources management. In: The Palgrave Handbook of Climate Resilient

Societies, Palgrave Macmillan, New York, pp. 1–19.

Timmerman, J., etal., 2017: Improving governance in transboundary cooperation

in water and climate change adaptation. Water Policy, 19(6), 1014–1029.

Tirado, M.C., 2017: Sustainable Diets for Healthy People and a Healthy Planet.

United Nations System Standing Committee on Nutrition, Italy, Rome.

Discussion paper.

Todd, N. and A.J. Valleron, 2015: Space-time Covariation of mortality with

temperature: a systematic study of deaths in France, 1968–2009. Environ.

Health Perspect., 123(7), 659–664, doi:10.1289/ehp.1307771.

Tompkins, A.M. and L. Caporaso, 2016a: Assessment of malaria transmission

changes in Africa, due to the climate impact of land use change using

Coupled Model Intercomparison Project Phase 5 earth system models.

Geospat. Health, 11(1 Suppl), 380, doi:10.4081/gh.2016.380.

Tompkins, A.M. and L. Caporaso, 2016b: Assessment of malaria transmission

changes in Africa, due to the climate impact of land use change using

Coupled Model Intercomparison Project Phase 5 earth system models.

Geospat. Health, 11, doi:10.4081/gh.2016.380.

Tong, S., et al., 2016: Climate change, food, water and population health

in China. Bull. World Health Organ., 94(10), 759–765, doi:10.2471/

blt.15.167031.

Torres, J.M. and J.A. Casey, 2017: The centrality of social ties to climate

migration and mental health. BMC Public Health, 17, 600–600, doi:10.1186/

s12889-017-4508-0.

Tosun, J., A. Jordan and M. Maor, 2017: Governing climate change: the (dis)

proportionality of policy responses. J. Environ. Policy Plan., 19(6), 596–598,

doi:10.1080/1523908x.2017.1354451.

Tozier de la Poterie, A., etal., 2020: Managing multiple hazards: lessons from

anticipatory humanitarian action for climate disasters during COVID-19.

Clim. Dev. DOI: https://doi.org/10.1080/17565529.2021.1927659.

Trewin, B.J., etal., 2020: Urban landscape features inﬂuence the movement and

distribution of the Australian container-inhabiting mosquito vectors Aedes

aegypti (diptera: Culicidae) and Aedes notoscriptus (diptera: Culicidae). J.

Med. Entomol., 57(2), 443–453, doi:10.1093/jme/tjz187.

Tschakert, P., et al., 2017: Climate change and loss, as if people mattered:

values, places, and experiences. WIREs Clim. Change, 8(5), e476–e476.

Tschakert, P., et al., 2019: One thousand ways to experience loss: a

systematic analysis of climate-related intangible harm from around the

world. Glob. Environ. Change Policy Dimens., 55, 58–72, doi:10.1016/j.

gloenvcha.2018.11.006.

Tshimanga, R.M., etal., 2021: An integrated information system of climate-

water-migrations-conﬂicts nexus in the Congo basin. Sustainability, 13(16),

9323.

Turco, M., N. Levin, N. Tessler and H. Saaroni, 2017: Recent changes and relations

among drought, vegetation and wildﬁres in the eastern Mediterranean: the

case of Israel. Glob. Planet. Change, 151, 28–35.

Turek-Hankins, L.L., etal., 2021: Climate change adaptation to extreme heat:

a global systematic review of implemented action. Oxf. Open Clim. Change,

1(1), 13, doi:10.1093/oxfclm/kgab005.

Turner, N. and K. Turner, 2008: “Where our women used to get the food”:

cumulative effects and loss of ethnobotanical knowledge and practice; case

study from coastal British Columbia. Botany, 86, 103-115..

Uboni, A., B. Ahman and J. Moen, 2020: Can management buffer pasture loss

and fragmentation for Sami reindeer herding in Sweden? Pastor. Policy

Pract., 10(1), doi:10.1186/s13570-020-00177-y.

Udas, B., etal., 2019: Basin level gendered vulnerabilities and adaptation: a

case of Gandaki River Basin. Environ. Dev., 31, 43–54.

Ulpiani, G., 2019: Water mist spray for outdoor cooling: a systematic review

of technologies, methods and impacts. Appl. Energy, 254, doi:10.1016/j.

apenergy.2019.113647.

UNDRR Asia-Paciﬁc, 2020: COVID-19 Brief: Combating the Dual Challenges of

Climate-related Disasters and COVID-19. United Nations Ofﬁce for Disaster

Risk Reduction, Bangkok, Thailand. 15 pp.UNEP, 2014: The Adaptation Gap

Report 2014. United Nations Environment Programme (UNEP), Nairobi,

Kenya.

UNEP, 2018: The Adaptation Gap Report 2018. United Nations Environment

Programme (UNEP), Nairobi, Kenya, ISBN 978-9280737288.

UNEP, 2021: Adaptation Gap Report. Nairobi, https://www.unep.org/resources/

adaptation-gap-report-2020. Last accessed 3 September 2021.

UNEP, European Union and Adelphi, 2019: Guidance Note and Tools to Address

Climate-Fragility Risks. UNEP, Nairobi.

UNFCCC, 2017a: Human health and adaptation: understanding climate impacts

on health and opportunities for action synthesis paper by the secretariat.

In: Nairobi Work Programme on Impacts, Vulnerability and Adaptation to

Climate Change. 03.03. Subsidiary Body for Scientiﬁc and Technological

Advice, Bonn.

UNFCCC, 2017b: Opportunities and Options for Integrating Climate Change

Adaptation with the Sustainable Development Goals and the Sendai

Framework for Disaster Risk Reduction 2015–2030. Technical paper by the

UNFCCC secretariat, Bonn.

UNICEF, 2021: The Climate Crisis is a Child Rights Crisis: Introducing the

Children’s Climate Risk Index. UNICEF, New York.

UNICEF et al, 2021: Joint Child Malnutrition Estimates—Levels and Trends –

2021 Edition. United Nations Children’s Fund, New York.

1166

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

UNICEF, WHO and WBG, 2019: United Nations Children’s Fund (UNICEF),

World Health Organization, International Bank for Reconstruction and

Development/The World Bank. Levels and Trends in Child Malnutrition:

Key Findings of the 2019 Edition Joint Child Malnutrition Estimates. World

Health Organization, Geneva.

United Nations Department of Economic and Social Affairs, Population Division

2019: World Population Prospects 2019. Department of Economic and social

affairs: Population Division. United Nations, New York.

Ürge-Vorsatz, D., T. Herrero, K. Dubash and F. Lecocq, 2014: Measuring the co-

beneﬁts of climate change mitigation. Annu. Rev. Environ. Resour., 39(6),

549–582, doi:10.1146/annurev-environ-031312-125456.

Ürge-Vorsatz, D., et al., 2016: Measuring multiple impacts of low-carbon

energy options in a green economy context. Appl. Energy, 179, 1409–1426,

doi:10.1016/j.apenergy.2016.07.027.

U.S. Global Change Research Program, 2016: The Impacts of Climate Change

on Human Health in the United States: a Scientiﬁc Assessment. U.S. Global

Change Research Program, Washington, DC, doi:10.7930/J0R49NQX.

Vahmani, P., A.D. Jones and C.M. Patricola, 2019: Interacting implications of

climate change, population dynamics, and urban heat mitigation for future

exposure to heat extremes. Environ. Res. Lett., 14(8), doi:10.1088/1748-

9326/ab28b0.

van den Hurk, B., etal., 2020: What Can COVID-19 Teach Us About Preparing for

Climate Risks in Europe? UN Ofﬁce for Disaster Risk Reduction, Geneva, 17

pp. https://climatestorylines.eu/wp-content/uploads/2020/12/Policy-Brief-

Lessons-from-COVID.pdf. Last accessed 3 September 2021.

van der Geest, K., et al., 2020: Climate change, ecosystem services and

migration in the Marshall Islands: are they related? Clim. Change, 16(1),

109–127.

van der Linden, S., 2015: The social-psychological determinants of climate

change risk perceptions: towards a comprehensive model. J. Environ.

Psychol., 41, 112–124, doi:10.1016/j.jenvp.2014.11.012.

van Dijk, M., T. Morley, M.L. Rau and Y. Saghai, 2021: A meta-analysis of

projected global food demand and population at risk of hunger for the period

2010–2050. Nat. Food, 2(7), 494–501, doi:10.1038/s43016-021-00322-9.

van Loenhout, J.A. F., et al., 2018: Heat and emergency room admissions in

the Netherlands. BMC Public Health, 18(1), 108, doi:10.1186/s12889-017-

5021-1.

van Valkengoed, A. and L. Steg, 2019: Meta-analyses of factors motivating

climate change adaptation behaviour. Nat. Clim. Change, 9(2), 158,

doi:10.1038/s41558-018-0371-y.

van Weezel, S., 2019: On climate and conﬂict: precipitation decline and

communal conﬂict in Ethiopia and Kenya. J. Peace Res., 56(4), 514–528.

Vandyck, T., etal., 2018: Air quality co-beneﬁts for human health and agriculture

counterbalance costs to meet Paris Agreement pledges. Nat. Commun., 9(1),

4939, doi:10.1038/s41467-018-06885-9.

Vanos, J., D.J. Vecellio and T. Kjellstrom, 2019: Workplace heat exposure, health

protection, and economic impacts: a case study in Canada. Am. J. Ind. Med.,

62(12), 1024–1037, doi:10.1002/ajim.22966.

Vardoulakis, S., etal., 2014: Comparative assessment of the effects of climate

change on heat- and cold-related mortality in the United Kingdom and

Australia. Environ. Health Perspect., 122(12), 1285–1292, doi:10.1289/

ehp.1307524.

Varghese, B.M., etal., 2019: The effects of ambient temperatures on the risk

of work-related injuries and illnesses: evidence from Adelaide, Australia

2003–2013. Environmental research, 170, 101-109.

Varghese, B.M., et al., 2020: Heat-related injuries in Australian workplaces:

perspectives from health and safety representatives. Saf. Sci, 126,

doi:10.1016/j.ssci.2020.104651.

Varotsos, K.V., C. Giannakopoulos and M. Tombrou, 2019: Ozone-temperature

relationship during the 2003 and 2014 heatwaves in Europe. Reg. Environ.

Change, 19(6), 1653–1665, doi:10.1007/s10113-019-01498-4.

Varquez, A.C.G., etal., 2020: Future increase in elderly heat-related mortality

of a rapidly growing Asian megacity. Sci. Rep., 10(1), doi:10.1038/s41598-

020-66288-z.

Vautard, R., et al., 2020: Human contribution to the record-breaking June

and July 2019 heatwaves in Western Europe. Environ. Res. Lett., 15(9),

doi:10.1088/1748-9326/aba3d4.

Velez-Valle, E.M., S. Echeverria and M. Santorelli, 2016: Type II diabetes

emergency room visits associated with Hurricane Sandy in New Jersey:

implications for preparedness. J. Environ. Health, 79(2), 30–37.

Venugopal, R. and S. Yasir, 2017: The politics of natural disasters in protracted

conﬂict: the 2014 ﬂood in Kashmir. Oxf. Dev. Stud., 45(4), 424–442.

Venugopal, V., etal., 2021: Epidemiological evidence from south Indian working

population-the heat exposures and health linkage. J. Expo. Sci. Environ.

Epidemiol., 31(1), 177–186, doi:10.1038/s41370-020-00261-w.

Verhaeghe, C., E. Feltes and J. Stacey, 2017: Nagmwedizk’an Gwanes Gangu

Ch’inidzed Ganexwilagh: the Fires Awakened Us. Tsilhqot’in National

Government, Williams Lake, BC, Canada.

Veronis, L., B. Boyd, R. Obokata and B. Main, 2018: Environmental change and

international migration: a review. In: Routledge Handbook of Environmental

Displacement and Migration [McLeman, R. and F. Gemenne]. Routledge,

London/New York.

Veronis, L. and R. McLeman, 2014: Environmental inﬂuences on African

migration to Canada: focus group ﬁndings from Ottawa-Gatineau. Popul.

Environ., 36(2), 234–251, doi:10.1007/s11111-014-0214-3.

Vestby, J., 2019: Climate variability and individual motivations for participating

in political violence. Glob. Environ. Change, 56, 114–123.

Vicedo-Cabrera, A.M., et al., 2018a: Temperature-related mortality impacts

under and beyond Paris Agreement climate change scenarios. Clim. Change,

150(3), 391–402, doi:10.1007/s10584-018-2274-3.

Vicedo-Cabrera, A.M., et al., 2021: The burden of heat-related mortality

attributable to recent human-induced climate change. Nat. Clim. Change,

11(6), 492, doi:10.1038/s41558-021-01058-x.

Vicedo-Cabrera, A.M., et al., 2018b: A multi-country analysis on potential

adaptive mechanisms to cold and heat in a changing climate. Environ. Int.,

111, 239–246, doi:10.1016/j.envint.2017.11.006.

Vidal, R., et al., 2014: Comparison of the carbon footprint of different

patient diets in a Spanish hospital. J. Health Serv. Res. Policy, 20,

doi:10.1177/1355819614553017.

Vik, M.H. and E. Carlquist, 2018: Measuring subjective well-being for

policy purposes: the example of well-being indicators in the WHO

“Health 2020” framework. Scand. J. Public Health, 46(2), 279–286,

doi:10.1177/1403494817724952.

Vinke, K., etal., 2017: Climatic risks and impacts in South Asia: extremes of

water scarcity and excess. Reg. Environ. Change, 17(6), 1569–1583.

Vins, H., J. Bell, S. Saha and J.J. Hess, 2015: The mental health outcomes of

drought: a systematic review and causal process diagram. Int. J. Environ. Res.

Public Health, 12(10), 13251–13275, doi:10.3390/ijerph121013251.

Viswanathan, B. and K. Kumar, 2015: Weather, agriculture and rural migration:

evidence from state and district level migration in India. Envir. Dev. Econ.,

20(4), 469–492, doi:10.1017/s1355770x1500008x.

Vlaskamp, D., et al., 2020: First autochthonous human West Nile virus

infections in the Netherlands, July to August 2020. Euro Surveill., 25(46),

doi:10.2807/1560-7917.Es.2020.25.46.2001904.

von Schneidemesser, E., C. Driscoll, H.E. Rieder and L.D. Schiferl, 2020: How

will air quality effects on human health, crops and ecosystems change in

the future? Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci., 378(2183),

doi:10.1098/rsta.2019.0330.

von Uexkull, N. and H. Buhaug, 2021: Security implications of climate

change: a decade of scientiﬁc progress. J. Peace Res., 58(1), 3–17,

doi:10.1177/0022343320984210.

Von Uexkull, N., M. Croicu, H. Fjelde and H. Buhaug, 2016: Civil conﬂict

sensitivity to growing-season drought. Proc. Natl. Acad. Sci., 113(44),

12391–12396.

1167

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Voordeckers, D., etal., 2021: Guidelines for passive control of trafﬁc-related air

pollution in street canyons: an overview for urban planning. Landsc. Urban

Plan., 207, doi:10.1016/j.landurbplan.2020.103980.

Vos T, etal., 2020: Global burden of 369 diseases and injuries in 204countries

and territories, 1990–2019: a systematic analysis for the Global Burden of

Disease Study 2019. The Lancet, 396(10598), 1204-1222

Vratnica, Z., et al., 2017: Haemorrhagic fever with renal syndrome in

Montenegro, 2004–14. Eur. J. Public. Health., 27(6), 1108–1110, doi:10.1093/

eurpub/ckx149.

Waha, K., etal., 2017: Climate change impacts in the Middle East and Northern

Africa (MENA) region and their implications for vulnerable population

groups. Reg. Environ. Change, 17(6), 1623–1638.

Waite, T.D., et al., 2017: The English national cohort study of ﬂooding and

health: cross-sectional analysis of mental health outcomes at year one. BMC

Public Health, 17, doi:10.1186/s12889-016-4000-2.

Walch, C., 2018: Disaster risk reduction amidst armed conﬂict: informal

institutions, rebel groups, and wartime political orders. Disasters, 42, S239–

S264, doi:10.1111/disa.12309.

Walkepää, A., 2019: Syntesrapport - En sammanställning av fyra samebyars

pilotprojekt med klimat- och sårbarhetsanalys samt handlingsplan för

klimatanpassning (Synthesis report – A compilation of four Sami villages’

pilot projects with climate and vulnerability analysis and an action plan for

climate adaptation). Giron-Kiruna, Sweden. Uppdragsnummer 12602183.

https://www.sametinget.se/klimat/syntesrapport Last accessed 3 September

2021.

Wallace, L.M., etal., 2015: Social vulnerability as a predictor of mortality and

disability: cross-country differences in the survey of health, aging, and

retirement in Europe (SHARE). Aging Clin. Exp. Res., 27(3), 365–372.

Walton, D. and M.K. van Aalst, 2020: Climate-related Extreme Weather Events

and COVID-19: A First Look at the Number of People Affected by Intersecting

Disasters. IFRC, Geneva. 21 pp.

Wang, H., et al., 2017a: Global, regional, and national under-5 mortality,

adult mortality, age-speciﬁc mortality, and life expectancy, 1970–2016: a

systematic analysis for the Global Burden of Disease Study 2016. Lancet,

390(10100), 1084–1150, doi:10.1016/S0140-6736(17)31833-0.

Wang, H., etal., 2020a: Global age-sex-speciﬁc fertility, mortality, healthy life

expectancy (HALE), and population estimates in 204countries and territories,

1950–2019: a comprehensive demographic analysis for the Global Burden

of Disease Study 2019. The Lancet, 396(10258), 1160–1203, doi:10.1016/

S0140-6736(20)30977-6.

Wang, J., N. Obradovich and S. Zheng, 2020b: A 43-million person investigation

into weather extremes and expressed sentiment under climate change. One

Earth, 2(6), 568-577..

Wang, P., W.B. Goggins and E.Y.Y. Chan, 2018a: Associations of Salmonella

hospitalizations with ambient temperature, humidity and rainfall in Hong

Kong. Environ. Int., 120, 223–230, doi:10.1016/j.envint.2018.08.014.

Wang, Q., etal., 2017: Environmental ambient temperature and blood pressure

in adults: a systematic review and meta-analysis. Sci. Total Environ., 575,

276–286, doi:10.1016/j.scitotenv.2016.10.019.

Wang, Q. et al. 2020: Independent and combined effects of heatwaves

and PM2.5 on preterm birth in Guangzhou, China: A survival analysis.

Environmental Health Perspectives. 128(1): 7006. DOI: 10.1289/EHP5117

Wang, X., et al., 2016: Ambient temperature and stroke occurrence: a

systematic review and meta-analysis. Int. J. Environ. Res. Public Health,

13(7), doi:10.3390/ijerph13070698.

Wang, X., E. Lavigne, H. Ouellette-kuntz and B. Chen, 2014: Acute impacts of

extreme temperature exposure on emergency room admissions related to

mental and behavior disorders in Toronto, Canada. J. Affect. Disord., 155,

154–161, doi:10.1016/j.jad.2013.10.042.

Wang, X., etal., 2020c: Global burden of respiratory infections associated with

seasonal inﬂuenza in children under 5years in 2018: a systematic review

and modelling study. Lancet Glob. Health, 8(4), E497–E510, doi:10.1016/

s2214-109x(19)30545-5.

Wang, X.L., et al., 2017c: Different responses of inﬂuenza epidemic to

weather factors among Shanghai, Hong Kong, and British Columbia. Int. J.

Biometeorol., 61(6), 1043–1053, doi:10.1007/s00484-016-1284-y.

Wang, Y., etal., 2018b: Accounting for adaptation and intensity in projecting

heat wave-related mortality. Environ. Res., 161, 464–471, doi:10.1016/j.

envres.2017.11.049.

Wang, Z., Z. Han, L. Liu and S. Yu, 2021: Place attachment and household

disaster preparedness: examining the mediation role of self-efﬁcacy. Int. J.

Environ. Res. Public Health, 18(11), doi:10.3390/ijerph18115565.

Wangdi, K., A.C.A. Clements, T. Du and S.V. Nery, 2018: Spatial and temporal

patterns of dengue infections in Timor-Leste, 2005–2013. Parasit Vectors,

11(1), 9, doi:10.1186/s13071-017-2588-4.

Warner, K., 2018: Coordinated approaches to large-scale movements of people:

contributions of the Paris Agreement and the Global Compacts for migration

and on refugees. Popul. Environ., 39(4), 384–401, doi:10.1007/s11111-018-

0299-1.

Watson, C.R., W. Matthew and S.T. Kirk, 2017: Public health preparedness

funding: key programs and trends from 2001 to 2017. Am. J. Public Health,

107(S2), S165–S167, doi:10.2105/ajph.2017.303963.

Watts, N., et al., 2017: The Lancet Countdown: tracking progress on health

and climate change. Lancet, 389(10074), 1151–1164, doi:10.1016/s0140-

6736(16)32124-9.

Watts, N., etal., 2015: Health and climate change: policy responses to protect

public health. Lancet, 386(10006), 1861–1914, doi:10.1016/s0140-

6736(15)60854-6.

Watts, N., etal., 2021: The 2020 report of the Lancet Countdown on health

and climate change: responding to converging crises. Lancet, 397(10269),

129–170, doi:10.1016/s0140-6736(20)32290-x.

Watts, N., etal., 2018a: The 2018 report of the Lancet Countdown on health

and climate change: shaping the health of nations for centuries to come.

Lancet, 392(10163), 2479–2514.

Watts, N., etal., 2019: The 2019 report of The Lancet Countdown on health and

climate change: ensuring that the health of a child born today is not deﬁned

by a changing climate. Lancet, 394(10211), 1836–1878, doi:10.1016/s0140-

6736(19)32596-6.

Watts, N., etal., 2018b: The Lancet Countdown on health and climate change:

from 25years of inaction to a global transformation for public health (vol

391, pg 581, 2017). Lancet, 391(10120), 540–540.

Weber, E., 2017: Trade agreements, labour mobility and climate change in the

Paciﬁc Islands. Reg. Environ. Change, 17, 1089–1101.

Weber, T., etal., 2020: Analysis of compound climate extremes and exposed

population in Africa under two different emission scenarios. Earths Future,

8(9), doi:10.1029/2019ef001473.

Weible, C., etal., 2020: COVID-19 and the policy sciences: initial reactions and

perspectives. Policy Sci., 53(2), 225–241, doi:10.1007/s11077-020-09381-4.

Weiler, F., 2019: Adaptation and health: are countries with more climate-

sensitive health sectors more likely to receive adaptation aid? Int. J. Environ.

Res. Public Health, 16(8), doi:10.3390/ijerph16081353.

Weinberger, K.R., etal., 2017: Projected temperature-related deaths in ten large

U.S. metropolitan areas under different climate change scenarios. Environ.

Int., 107, 196–204, doi:10.1016/j.envint.2017.07.006.

Weinberger, K.R., et al., 2018a: Projected changes in temperature-related

morbidity and mortality in Southern New England. Epidemiology, 29(4),

473–481, doi:10.1097/ede.0000000000000825.

Weinberger, K.R., A. Zanobetti, J. Schwartz and G.A. Wellenius, 2018b:

Effectiveness of National Weather Service heat alerts in preventing mortality

in 20 US cities. Environ. Int., 116, 30–38, doi:10.1016/j.envint.2018.03.028.

Weisent, J., W. Seaver, A. Odoi and B. Rohrbach, 2014: The importance of climatic

factors and outliers in predicting regional monthly campylobacteriosis risk in

Georgia, USA. Int. J. Biometeorol., 58(9), 1865–1878, doi:10.1007/s00484-

014-0788-6.

1168

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Weitensfelder, L. and H. Moshammer, 2020: Evidence of adaptation to increasing

temperatures. Int. J. Environ. Res. Public Health, 17(1), doi:10.3390/

ijerph17010097.

Wells, M.L., et al., 2020: Future HAB science: directions and challenges

in a changing climate. Harmful Algae, 91, 101632, doi:10.1016/j.

hal.2019.101632.

Wells, M.L., etal., 2015: Harmful algal blooms and climate change: learning

from the past and present to forecast the future. Harmful Algae, 49, 68–93,

doi:10.1016/j.hal.2015.07.009.

Weyant, C., etal., 2018: Anticipated burden and mitigation of carbon-dioxide-

induced nutritional deﬁciencies and related diseases: a simulation modeling

study. PLoS Med., 15(7), e1002586, doi:10.1371/journal.pmed.1002586.

Whaley, A.L., 2009: Trauma among survivors of hurricane Katrina: considerations

and recommendations for mental health care. J. Loss Trauma., 14(6), 459–

476, doi:10.1080/15325020902925480.

Wheeler, N. and N. Watts, 2018: Climate change: from science to practice. Curr.

Environ. Health Rep., 5(1), 170–178, doi:10.1007/s40572-018-0187-y.

Whitmee, S., et al., 2015: Safeguarding human health in the Anthropocene

epoch: report of The Rockefeller Foundation-Lancet Commission on

planetary health. Lancet, 386(10007), 1973–2028, doi:10.1016/s0140-

6736(15)60901-1.

WHO. and International Water Association, 2009: Water Safety Plan Manual:

Step-by-step Risk Management for Drinking-water Suppliers. [Who (ed.)].

Geneva, Switzerland, https://apps.who.int/iris/handle/10665/75141.

WHO, 1946: Preamble to the Constitution of WHO as Adopted by the

International Health Conference, New York, 19 June – 22 July 1946; Signed

on 22 July 1946 by the Representatives of 61 States and Entered into Force

on 7 April 1948. Ofﬁcial Records of WHO, New York. 100 pp.

WHO, 2013: Protecting Health from Climate Change: Vulnerability and

Adaptation Assessment. WHO, Geneva.

WHO, 2014: Quantitative Risk Assessment of the effects of Climate Change on

Selected Causes of Death, 2030s and 2050s. WHO, Geneva.

WHO, 2015a: Operational Framework for Building Climate Resilient Health

Systems. World Health Organization, Geneva.

WHO, 2015b: Climate and Health Country Proﬁles – 2015 a Global Overview.

WHO, Geneva. 24 pp.

WHO, 2015c: Reducing Global Health Risks Through Mitigation of Short Lived

Climate Pollutants, 1–121. WHO, Geneva.

WHO., 2016a: Sanitation Safety Planning: Manual for Safe Use and Disposal

of Wastewater, Greywater and Excreta. WHO, Geneva, https://www.who.int/

water_sanitation_health/publications/ssp-manual/en/.

WHO, 2016b: World Health Statistics 2016, Chapter 6 Sustainable development

goals health and health related targets. WHO, Geneva. https://www.who.int/

gho/publications/world_health_statistics/2016/EN_WHS2016_Chapter6.

pdf. Last accessed 3 September 2021.

WHO., 2018a: COP24 Special Report on Health and Climate Change. WHO,

Geneva.

WHO, 2018b: WHO Estimates of the Global Burden of Foodborne Diseases.

World Health Organization, https://apps.who.int/iris/bitstream/

handle/10665/199350/9789241565165_eng.pdf.. Last accessed 3

September 2021.

WHO, 2018c: WASH and Health Working Together: a ‘How-to’ Guide for

Neglected Tropical Disease Programmes. WHO, Geneva.

WHO., 2019a: The WHO Special Initiative for Mental Health (2019–2023):

Universal Health Coverage for Mental health. WHO, Geneva.

WHO, 2019b: WHO Health and Climate Change Survey Report: Tracking Global

Progress. World Health Organization, Geneva.

WHO, 2020a: Who Guidance for Climate Resilient and Environmentally

Sustainable Health Care Facilities. World Health Organization, Geneva.

WHO, 2020b: WHO Manifesto for a Healthy Recovery from COVID-19:Prescriptions

and Actionables for a Healthy and Green Recovery. [Who (ed.)].https://www.

who.int/publications/i/item/who-manifesto-healthy-recovery-covid19 . Last

accessed 3 September 2021.

WHO, 2020c: Joint Child Malnutrition Estimates. WHO, Geneva.

WHO, 2021a: Stronger Collaboration for an Equitable and Resilient Recovery

Towards the Health-Related Sustainable Development Goals: 2021 Progress

Report on the Global Action Plan for Healthy Lives and Well-Being for All.

WHO, Geneva.

WHO, 2021b: Universal Health Coverage (UHC). WHO, Geneva.

WHO, 2021c: World Health Statistics 2021: Monitoring Health for the SDGs,

Sustainable Development Goals. WHO, Geneva.

WHO, 2021d.WHO Household Energy Database. WHO, Geneva. https://www.

who.int/news-room/fact-sheets/detail/household-air-pollution-and-health

Last accessed 3 September 2021.

Whyte, K., 2018: Critical investigations of resilience: a brief introduction to

indigenous environmental studies & sciences. Daedalus, 147(2), 136–147,

doi:10.1162/DAED_a_00497.

Wickramage, K., et al., 2018: Migration and health: a global public health

research priority. BMC Public Health, 18, doi:10.1186/s12889-018-5932-5.

Wiederkehr, C., M. Beckmann and K. Hermans, 2018: Environmental change,

adaptation strategies and the relevance of migration in Sub-Saharan

drylands. Environ. Res. Lett., 13(11).DOI: https://doi.org/10.1088/1748-9326/

aae6de. Last accessed 3 September 2021

Wiegel, H., I. Boas and J. Warner, 2019: A mobilities perspective on migration in

the context of environmental change. Wiley Interdiscip. Rev. Clim. Change,

10(6), e610.

Wilder, J., etal., 2017: Polar bear attacks on humans: implications of a changing

climate. Wildl. Soc. Bull., 41(3), 537–547, doi:10.1002/wsb.783.

Willett, W., etal., 2019: Food in the Anthropocene: the EAT-Lancet Commission

on healthy diets from sustainable food systems. Lancet, 393(10170), 447–

492, doi:10.1016/s0140-6736(18)31788-4.

Willi, Y., G. Nischik, D. Braunschweiger and M. Putz, 2020: Responding to the

COVID-19 crisis: transformative governance in Switzerland. Tijdschr. Econ.

Soc. Geogr., 111(3), 302–317, doi:10.1111/tesg.12439.

Williams, L., 2018: Climate change, colonialism, and women’s well-being

in Canada: what is to be done? Can. J. Public Health, 109(2), 268–271,

doi:10.17269/s41997-018-0031-z.

Williams, M.L., etal., 2018: The Lancet Countdown on health beneﬁts from the

UK Climate Change Act: a modelling study for Great Britain. Lancet Planet.

Health, 2(5), e202–e213, doi:10.1016/s2542-5196(18)30067-6.

Williams, M.N., S.R. Hill and J. Spicer, 2015a: Will Climate Change Increase or

Decrease Suicide Rates? The Differing Effects of Geographical, Seasonal,

and Irregular Variation in Temperature on Suicide Incidence. Springer,

Netherlands, 1–10.

Williams, M.S., et al., 2015b: Temporal patterns of Campylobacter

contamination on chicken and their relationship to campylobacteriosis cases

in the United States. Int. J. Food Microbiol., 208, 114–121, doi:10.1016/j.

ijfoodmicro.2015.05.018.

Williams, S., etal., 2020: Workers’ health and safety in the heat: current practice

in Australian workplaces. Policy Pract. Health Saf., 18(2), 67–79, doi:10.108

0/14773996.2020.1765610.

Williamson, C., etal., 2017: Climate change-induced increases in precipitation

are reducing the potential for solar ultraviolet radiation to inactivate

pathogens in surface waters. Sci. Rep., 7, doi:10.1038/s41598-017-13392-2.

Wilmsen, B. and M. Webber, 2015: What can we learn from the practice

of development-forced displacement and resettlement for organised

resettlements in response to climate change? Geoforum, 58, 76–85.

Winkler, R.L. and R. M. D., 2021: Amenities or disamenities? Estimating the

impacts of extreme heat and wildﬁre on domestic US migration. Popul.

Environ., 42(4), 622–648, doi:10.1007/s11111-020-00364-4.

Winne, J.D. and G. Peersman, 2019: The impact of food prices on conﬂict

revisited. J. Bus. Econ. Stat.,39(2), 1–14.

Winsemius, H., etal., 2018: Disaster risk, climate change, and poverty: assessing

the global exposure of poor people to ﬂoods and droughts. Envir. Dev. Econ.,

23(3), 328–348, doi:10.1017/s1355770x17000444.

1169

Health, Wellbeing and the Changing Structure of Communities Chapter 7

Wischnath, G. and H. Buhaug, 2014: Rice or riots: on food production and

conﬂict severity across India. Polit. Geogr., 43, 6–15, doi:10.1016/j.

polgeo.2014.07.004.

Witmer, F.D., etal., 2017: Subnational violent conﬂict forecasts for sub-Saharan

Africa, 2015–65, using climate-sensitive models. J. Peace Res., 54(2), 175–

192.

Witt, C., etal., 2015: The effects of climate change on patients with chronic

lung disease. Dtsch. Arztebl. Online, doi:10.3238/arztebl.2015.0878.

Wondmagegn, B., etal., 2019: What do we know about the healthcare costs

of extreme heat exposure? A comprehensive literature review. Sci. Total

Environ., 657, 608–618, doi:10.1016/j.scitotenv.2018.11.479.

Wood, R.M. and T.M. Wright, 2016: Responding to catastrophe: repression

dynamics following rapid-onset natural disasters. J. Conﬂ. Resolut., 60(8),

1446–1472.

Woodcock, J., etal., 2018: Development of the Impacts of Cycling Tool (ICT):

A modelling study and web tool for evaluating health and environmental

impacts of cycling uptake. PLoS Med., 15(7), e1002622, doi:10.1371/journal.

pmed.1002622.

Woodward, A., etal., 2019: Population health impacts of China’s climate change

policies. Environ. Res., 175, 178–185, doi:10.1016/j.envres.2019.05.020.

Woodward, A.J. and J.M. Samet, 2018: Climate change, hurricanes, and health.

Am. J. Public Health, 108(1), 33–35, doi:10.2105/ajph.2017.304197.

World Bank., 2021: Global Economic Prospects, June 2021. World Bank,

Washington DC, ISBN 978-1464816659.

World Bank,, 2021: Indigenous Peoples. World Bank, Washington, DC. https://

www.worldbank.org/en/topic/indigenouspeoples#1. Last accessed 3

September 2021.

Wouters, H., et al., 2017: Heat stress increase under climate change twice

as large in cities as in rural areas: a study for a densely populated

midlatitude maritime region. Geophys. Res. Lett., 44(17), 8997–9007,

doi:10.1002/2017gl074889.

Wrathall, D. and N. Suckall, 2016: Labour migration amidst ecological change.

Migr. Dev., 5(2), 314–329.

Wright, C., etal., 2021: A transdisciplinary approach to address climate change

adaptation for human health and well-being in Africa. Int. J. Environ. Res.

Public Health, 18(8), doi:10.3390/ijerph18084258.

Wright, N., etal., 2020: Health emergency and disaster risk management: ﬁve

years into implementation of the Sendai framework. Int. J. Disaster Risk Sci.,

11(2), 206–217, doi:10.1007/s13753-020-00274-x.

Wu, F., etal., 2011: Climate change impacts on mycotoxin risks in US maize.

World Mycotoxin J., 4(1), 79–93, doi:10.3920/wmj2010.1246.

Wu, J., M. Yunus, P.K. Streatﬁeld and M. Emch, 2014: Association of climate

variability and childhood diarrhoeal disease in rural Bangladesh, 2000–2006.

Epidemiol. Infect., 142(9), 1859–1868, doi:10.1017/s095026881300277x.

Xiang, J., etal., 2018: Impact of meteorological factors on hemorrhagic fever

with renal syndrome in 19 cities in China, 2005–2014. Sci. Total Environ.,

636, 1249–1256, doi:10.1016/j.scitotenv.2018.04.407.

Xiang, J.J., P. Bi, D. Pisaniello and A. Hansen, 2014: Health impacts of workplace

heat exposure: an epidemiological review. Ind. Health, 52(2), 91–101,

doi:10.2486/indhealth.2012-0145.

Xiao, H., etal., 2014: Animal reservoir, natural and socioeconomic variations

and the transmission of hemorrhagic fever with renal syndrome in Chenzhou,

China, 2006–2010. PLoS Negl. Trop. Dis., 8(1), e2615, doi:10.1371/journal.

pntd.0002615.

Xie, Y., etal., 2018: Co-beneﬁts of climate mitigation on air quality and human

health in Asian countries. Environ. Int., 119, 309–318, doi:10.1016/j.

envint.2018.07.008.

Xiong, J., etal., 2020: Impact of COVID-19 pandemic on mental health in the

general population: a systematic review. J. Affect. Disord., 277, 55–64,

doi:10.1016/j.jad.2020.08.001.

Xu, C., etal., 2020: Future of the human climate niche. Proc. Natl. Acad. Sci.,

117, 201910114, doi:10.1073/pnas.1910114117.

Xu, Y.Z., W.D. Yang and J.Z. Wang, 2017: Air quality early-warning system

for cities in China. Atmos. Environ., 148, 239–257, doi:10.1016/j.

atmosenv.2016.10.046.

Xu, Z., etal., 2019a: Spatiotemporal patterns and climatic drivers of severe

dengue in Thailand. Sci. Total Environ., 656, 889–901, doi:10.1016/j.

scitotenv.2018.11.395.

Xu, Z., etal., 2014: The impact of heat waves on children’s health: a systematic

review. Int. J. Biometeorol., 58(2), 239–247, doi:10.1007/s00484-013-

0655-x.

Xu, Z., et al., 2019b: Heatwaves and diabetes in Brisbane, Australia: a

population-based retrospective cohort study. Int. J. Epidemiol., 48(4), 1091–

1100.

le Xuan, T.T., P. Van Hau, T. do Thu and T.T. do Toan, 2014: Estimates of

meteorological variability in association with dengue cases in a coastal city

in northern Vietnam: an ecological study. Glob. Health Action, 7, 23119,

doi:10.3402/gha.v7.23119.

Yactayo, S., etal., 2016: Epidemiology of Chikungunya in the Americas. J. Infect.

Dis., 214(suppl 5), S441–S445, doi:10.1093/infdis/jiw390.

Yamana, T.K., A. Bomblies and E.A.B. Eltahir, 2016: Climate change unlikely to

increase malaria burden in West Africa. Nat. Clim. Change, 6, 1009–1013.

Yan, X., etal., 2018: Effects of early-life malnutrition on neurodevelopment

and neuropsychiatric disorders and the potential mechanisms. Prog.

Neuropsychopharmacol. Biol. Psychiatry, 83, 64–75.

Yang, G. and R. Bergquist, 2018: Potential impact of climate change on

schistosomiasis: a global assessment attempt. Trop. Med. Infect. Dis., 3(4),

doi:10.3390/tropicalmed3040117.

Yang, J., et al., 2019: Heatwave and mortality in 31 major Chinese cities:

deﬁnition, vulnerability and implications. Sci. Total Environ., 649, 695–702,

doi:10.1016/j.scitotenv.2018.08.332.

Yang, J., etal., 2016: The effect of ambient temperature on diabetes mortality

in China: a multi-city time series study. Sci. Total Environ., 543(Pt A), 75–82,

doi:10.1016/j.scitotenv.2015.11.014.

Yang, J., etal., 2021: Projecting heat-related excess mortality under climate

change scenarios in China. Nat. Commun., 12(1), doi:10.1038/s41467-021-

21305-1.

Yang, Z. and J. Wang, 2017: A new air quality monitoring and early warning

system: air quality assessment and air pollutant concentration prediction.

Environ. Res., 158, 105–117, doi:10.1016/j.envres.2017.06.002.

Yasjukevich, V.V., I.O. Popov, S.N. Titkina and N.V. Yasjukevich, 2018: The

vulnerability of subjects of the Russian Federation in relation to the

distribution of the main vectors of Ixodes tick-borne borelliosis and tick-

borne encephalitis in the expected climate change. Probl. Ecol. Monit.

Ecosyst. Model., xix(4), 8–28.

Yeargin, S.W., etal., 2019: Epidemiology of exertional heat illnesses in national

collegiate athletic association athletes during the 2009–2010 through

2014–2015 academic years. J. Athl. Train., 54(1), 55–63, doi:10.4085/1062-

6050-504-17.

Yeni, F. and H. Alpas, 2017: Vulnerability of global food production to extreme

climatic events. Food Res. Int., 96, 27–39, doi:10.1016/j.foodres.2017.03.020.

Yin, Q., etal., 2019: Mapping the increased minimum mortality temperatures

in the context of global climate change. Nat. Commun., 10, doi:10.1038/

s41467-019-12663-y.

Yoo, E.H., et al., 2021: Association between extreme temperatures and

emergency room visits related to mental disorders: a multi-region time-

series study in New York, USA. Sci. Total Environ. https://doi.org/10.1016/j.

scitotenv.2021.148246

Yorifuji, T., etal., 2017: Prenatal exposure to outdoor air pollution and child

behavioral problems at school age in Japan. Environ. Int., 99, 192–198,

doi:10.1016/j.envint.2016.11.016.

Younan, D., etal., 2018: Longitudinal analysis of particulate air pollutants and

adolescent delinquent behavior in Southern California. J. Abnorm. Child

Psychol., 46(6), 1283–1293.

1170

Chapter 7 Health, Wellbeing and the Changing Structure of Communities

Young, H.S., etal., 2017: Interacting effects of land use and climate on rodent-

borne pathogens in central Kenya. Philos. Trans. Royal Soc. B Biol. Sci.,

372(1722), doi:10.1098/rstb.2016.0116.

Young, I., B.A. Smith and A. Fazil, 2015: A systematic review and meta-analysis

of the effects of extreme weather events and other weather-related variables

on Cryptosporidium and Giardia in fresh surface waters. J. Water Health,

13(1), 1–17, doi:10.2166/wh.2014.079.

Youssouf, H., etal., 2014: Non-accidental health impacts of wildﬁre smoke.

Int. J. Environ. Res. Public Health, 11(11), 11772–11804, doi:10.3390/

ijerph111111772.

Yuan, L., K. Shin and S. Managi, 2018: Subjective well-being and environmental

quality: the impact of air pollution and green coverage in China. Ecol. Econ.,

153, 124–138, doi:10.1016/j.ecolecon.2018.04.033.

Yun, J., etal., 2016: Association between the ambient temperature and the

occurrence of human Salmonella and Campylobacter infections. Sci Rep, 6,

28442, doi:10.1038/srep28442.

Zadnik, V., et al., 2020: Impact of COVID-19 on cancer diagnosis and

management in Slovenia – preliminary results. Radiol. Oncol., 54(3), 329–

334, doi:10.2478/raon-2020-0048.

Zaitchik, B.F., 2019: Climate and health across Africa. In: Oxford Research

Encyclopedia, Climate Science. Oxford University Press, Oxford. doi:10.1093/

acrefore/9780190228620.013.555.

Zander, K., C. Richerzhagen and S. Garnett, 2019: Human mobility intentions

in response to heat in urban South East Asia. Glob. Environ. Change Hum.

Policy Dimens., 56, 18–28, doi:10.1016/j.gloenvcha.2019.03.004.

Zavaleta-Cortijo, C., etal., 2020: Climate change and COVID-19: reinforcing

Indigenous food systems. Lancet Planet. Health, 4(9), E381–E382,

doi:10.1016/s2542-5196(20)30173-x.

Zavaleta, C., et al., 2018: Multiple non-climatic drivers of food insecurity

reinforce climate change maladaptation trajectories among Peruvian

Indigenous Shawi in the Amazon. PLoS ONE, 13(10), doi:10.1371/journal.

pone.0205714.

Zeng, J., C. Lu and Q.H. Deng, 2017: Prenatal exposure to diurnal temperature

variation and early childhood pneumonia. J. Therm. Biol., 65, 105–112,

doi:10.1016/j.jtherbio.2017.02.012.

Zhang, B., G. Li, Y. Ma and X. Pan, 2018a: Projection of temperature-related

mortality due to cardiovascular disease in Beijing under different climate

change, population, and adaptation scenarios. Environ. Res., 162, 152–159,

doi:10.1016/j.envres.2017.12.027.

Zhang, J., etal., 2020a: The effects of elevated carbon dioxide concentration

and mental workload on task performance in an enclosed environmental

chamber. Build. Environ., 178, 106938, doi:10.1016/j.buildenv.2020.106938.

Zhang, N., etal., 2019a: The impact of the 2016 ﬂood event in Anhui Province,

China on infectious diarrhea disease: an interrupted time-series study.

Environ. Int., 127, 801–809, doi:10.1016/j.envint.2019.03.063.

Zhang, Y., S. Isukapalli, L. Bielory and P. Georgopoulos, 2013: Bayesian analysis

of climate change effects on observed and projected airborne levels of birch

pollen. Atmos. Environ., 68, 64–73, doi:10.1016/j.atmosenv.2012.11.028.

Zhang, Y., etal., 2017a: Co-beneﬁts of global, domestic, and sectoral greenhouse

gas mitigation for US air quality and human health in 2050. Environ. Res.

Lett., 12(11), doi:10.1088/1748-9326/aa8f76.

Zhang, Y., C. Yu and L. Wang, 2017b: Temperature exposure during pregnancy

and birth outcomes: an updated systematic review of epidemiological

evidence. Environ. Pollut., 225, 700–712, doi:10.1016/j.envpol.2017.02.066.

Zhang, Y.Q., M.J. Peng, L. Wang and C.H. Yu, 2018b: Association of diurnal

temperature range with daily mortality in England and Wales: a nationwide

time-series study. Sci. Total Environ., 619, 291–300, doi:10.1016/j.

scitotenv.2017.11.056.

Zhang, Y.Q., et al., 2019b: Socio-geographic disparity in cardiorespiratory

mortality burden attributable to ambient temperature in the United States.

Environ. Sci. Pollut. Res., 26(1), 694–705, doi:10.1007/s11356-018-3653-z.

Zhang, Z., T. Xue and X. Jin, 2020b: Effects of meteorological conditions and air

pollution on COVID-19 transmission: evidence from 219 Chinese cities. Sci.

Total Environ., 741, 140244, doi:10.1016/j.scitotenv.2020.140244.

Zhao, L., etal., 2021: Global multi-model projections of local urban climates.

Nat. Clim. Change, 11(2), doi:10.1038/s41558-020-00958-8.

Zhao, N., G. Cao, J.K. Vanos and D.J. Vecellio, 2018a: The effects of synoptic

weather on inﬂuenza infection incidences: a retrospective study utilizing

digital disease surveillance. Int. J. Biometeorol., 62(1), 69–84, doi:10.1007/

s00484-017-1306-4.

Zhao, Q., etal., 2019: Temperature variability and hospitalization for ischaemic

heart disease in Brazil: a nationwide case-crossover study during 2000–2015.

Sci. Total Environ., 664, 707–712, doi:10.1016/j.scitotenv.2019.02.066.

Zhao, T., et al., 2018b: Ambient ozone exposure and mental health: a

systematic review of epidemiological studies. Environ. Res., 165, 459–472,

doi:10.1016/j.envres.2018.04.015.

Zhao, X., etal., 2014: Japanese encephalitis risk and contextual risk factors

in southwest China: a Bayesian hierarchical spatial and spatiotemporal

analysis. Int. J. Environ. Res. Public Health, 11(4), 4201–4217, doi:10.3390/

ijerph110404201.

Zheng, X.R., etal., 2021: Preconceptional and prenatal exposure to diurnal

temperature variation increases the risk of childhood pneumonia. BMC

Pediatr., 21(1), doi:10.1186/s12887-021-02643-x.

Zhong, S., etal., 2020: Assessing the effectiveness and pathways of planned

shelters in protecting mental health of ﬂood victims in China. Environ. Res.

Lett. DOI: https://doi.org/10.1088/1748-9326/abc446.

Zhong, S., etal., 2018: The long-term physical and psychological health impacts

of ﬂooding: a systematic mapping. Sci. Total Environ., 626, 165–194,

doi:10.1016/j.scitotenv.2018.01.041.

Zhu, C., etal., 2018: Carbon dioxide (CO2) levels this century will alter the

protein, micronutrients, and vitamin content of rice grains with potential

health consequences for the poorest rice-dependent countries. Sci. Adv.,

4(5), eaaq1012, doi:10.1126/sciadv.aaq1012.

Zickgraf, 2019: Keeping people in place: political factors of (Im)mobility and

climate change. Soc. Sci., 8, 228, doi:10.3390/socsci8080228.

Zickgraf, C., 2018: Immobility [McLeman, R. and F. Gemenne (eds.)]. Routledge,

London.

Zickgraf, C., et al., 2016: The Impact of Vulnerability and Resilience to

Environmental Changes on Mobility Patterns in West Africa. World Bank,

Washington, DC.

Ziegler, U., etal., 2020: West Nile virus epidemic in Germany triggered by

epizootic emergence, 2019. Viruses, 12(4), doi:10.3390/v12040448.

Zilbermint, M., 2020: Diabetes and climate change. J. Commun. Hosp. Intern.

Med. Perspect., 10(5), 409–412, doi:10.1080/20009666.2020.1791027.

Zinsstag, J., etal., 2018: Climate change and One Health. FEMS Microbiol. Lett.,

365(11), doi:10.1093/femsle/fny085.

Ziska, L.H., 2020: An overview of rising CO2 and climatic change on

aeroallergens and allergic diseases. Allergy, Asthma & Immunology Research

12(5). DOI: 10.4168/aair.2020.12.5.771

Ziska, L.H., etal., 2019: Temperature-related changes in airborne allergenic

pollen abundance and seasonality across the northern hemisphere: a

retrospective data analysis. Lancet Planet. Health, 3(3), e124–e131,

doi:10.1016/s2542-5196(19)30015-4.

Zorrilla, C.D., 2017: The view from Puerto Rico — Hurricane Maria and

its aftermath. N. Engl. J. Med., 377(19), 1801–1803, doi:10.1056/

NEJMp1713196.

Zuvela-Aloise, M., R. Koch, S. Buchholz and B. Fruh, 2016: Modelling the

potential of green and blue infrastructure to reduce urban heat load in the

city of Vienna. Clim. Change, 135(3-4), 425–438, doi:10.1007/s10584-016-

1596-2.